social media research studies

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Social Media Use And Well-Being: A Systematic Review And Meta-Analysis

Affiliations.

• 1 Jamia Millia Islamia, New Delhi, India.
• 2 Qazvin University of Medical Sciences, Qazvin, Iran.
• PMID: 39190573
• DOI: 10.1089/cyber.2024.0001

Prior research has investigated the link between social media use (SMU) and negative well-being. However, the connection with positive well-being has not been extensively studied, leading to a situation where there are inconsistent and inconclusive findings. This study fills this gap by examining the correlation between excessive and problematic SMU and subjective as well as psychological well-being (PWB). We conducted a systematic search across databases such as PubMed, Scopus, and Web of Science, and gray literature sources such as Research Gate and ProQuest, yielding 51 relevant studies for meta-analysis, encompassing a sample size of 680,506 individuals. Employing the Newcastle-Ottawa Scale, we assessed study quality, whereas statistical analysis was executed using R Studio. Excessive SMU showed no significant association with subjective ( ES = 0.003, 95% confidence interval [ 95% CI ]: -0.08, 0.09; p = 0.94, I 2 = 95.8%, k =16) and PWB ( ES = 0.16, 95% CI : -0.15, 0.45; p = 0.26, I 2 = 98%, k = 7). Conversely, problematic SMU showed a negative correlation with subjective ( ES = -0.14 , 95% CI : -0.20, -0.09; p = 0.00, I 2 = 93.3%, k = 25) and PWB ( ES = -0.19 , 95% CI : -0.31, -0.06; p = 0.01, I 2 = 95%, k = 5), with two outliers removed. No publication bias was detected. Subgroup analysis highlighted effects of "sampling method" ( p < 0.05), "study quality" ( p < 0.05), "developmental status" ( p < 0.05), "forms of social media" ( p < 0.05), and "type of population" ( p < 0.01) on the estimated pooled effect sizes. Although univariate meta-regression showed the effects of "% of Internet users" ( p < 0.05) and "male%" ( p < 0.05), and multivariate meta-regression showed the combined effect of moderators only on the relationship between problematic SMU and subjective well-being.

Keywords: excessive social media use; meta-analysis; problematic social media use; psychological well-being; subjective well-being.

PubMed Disclaimer

Publication types

• Search in MeSH

LinkOut - more resources

Full text sources, miscellaneous.

• NCI CPTAC Assay Portal
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Advertisement

Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

• Published: 20 April 2020
• Volume 5 , pages 245–257, ( 2020 )

Cite this article

• John A. Naslund 1 ,
• Ameya Bondre 2 ,
• John Torous 3 &
• Kelly A. Aschbrenner 4  

404k Accesses

219 Citations

185 Altmetric

19 Mentions

Explore all metrics

Avoid common mistakes on your manuscript.

Introduction

Social media has become a prominent fixture in the lives of many individuals facing the challenges of mental illness. Social media refers broadly to web and mobile platforms that allow individuals to connect with others within a virtual network (such as Facebook, Twitter, Instagram, Snapchat, or LinkedIn), where they can share, co-create, or exchange various forms of digital content, including information, messages, photos, or videos (Ahmed et al. 2019 ). Studies have reported that individuals living with a range of mental disorders, including depression, psychotic disorders, or other severe mental illnesses, use social media platforms at comparable rates as the general population, with use ranging from about 70% among middle-age and older individuals to upwards of 97% among younger individuals (Aschbrenner et al. 2018b ; Birnbaum et al. 2017b ; Brunette et al. 2019 ; Naslund et al. 2016 ). Other exploratory studies have found that many of these individuals with mental illness appear to turn to social media to share their personal experiences, seek information about their mental health and treatment options, and give and receive support from others facing similar mental health challenges (Bucci et al. 2019 ; Naslund et al. 2016b ).

Across the USA and globally, very few people living with mental illness have access to adequate mental health services (Patel et al. 2018 ). The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to address these shortfalls in existing mental health care, by enhancing the quality, availability, and reach of services. Recent studies have explored patterns of social media use, impact of social media use on mental health and wellbeing, and the potential to leverage the popularity and interactive features of social media to enhance the delivery of interventions. However, there remains uncertainty regarding the risks and potential harms of social media for mental health (Orben and Przybylski 2019 ) and how best to weigh these concerns against potential benefits.

In this commentary, we summarized current research on the use of social media among individuals with mental illness, with consideration of the impact of social media on mental wellbeing, as well as early efforts using social media for delivery of evidence-based programs for addressing mental health problems. We searched for recent peer reviewed publications in Medline and Google Scholar using the search terms “mental health” or “mental illness” and “social media,” and searched the reference lists of recent reviews and other relevant studies. We reviewed the risks, potential harms, and necessary safety precautions with using social media for mental health. Overall, our goal was to consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, while balancing the need for safety. Given this broad objective, we did not perform a systematic search of the literature and we did not apply specific inclusion criteria based on study design or type of mental disorder.

Social Media Use and Mental Health

In 2020, there are an estimated 3.8 billion social media users worldwide, representing half the global population (We Are Social 2020 ). Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones (Firth et al. 2015 ; Glick et al. 2016 ; Torous et al. 2014a , b ). Similarly, there is mounting evidence showing high rates of social media use among individuals with mental disorders, including studies looking at engagement with these popular platforms across diverse settings and disorder types. Initial studies from 2015 found that nearly half of a sample of psychiatric patients were social media users, with greater use among younger individuals (Trefflich et al. 2015 ), while 47% of inpatients and outpatients with schizophrenia reported using social media, of which 79% reported at least once-a-week usage of social media websites (Miller et al. 2015 ). Rates of social media use among psychiatric populations have increased in recent years, as reflected in a study with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental illness in treatment as compared with low-income groups from the general population (Brunette et al. 2019 ).

Similarly, among individuals with serious mental illness receiving community-based mental health services, a recent study found equivalent rates of social media use as the general population, even exceeding 70% of participants (Naslund et al. 2016 ). Comparable findings were demonstrated among middle-age and older individuals with mental illness accessing services at peer support agencies, where 72% of respondents reported using social media (Aschbrenner et al. 2018b ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study (Abdel-Baki et al. 2017 ).

Individuals who self-identified as having a schizophrenia spectrum disorder responded to a survey shared through the National Alliance of Mental Illness (NAMI) and reported that visiting social media sites was one of their most common activities when using digital devices, taking up roughly 2 h each day (Gay et al. 2016 ). For adolescents and young adults ages 12 to 21 with psychotic disorders and mood disorders, over 97% reported using social media, with average use exceeding 2.5 h per day (Birnbaum et al. 2017b ). Similarly, in a sample of adolescents ages 13–18 recruited from community mental health centers, 98% reported using social media, with YouTube as the most popular platform, followed by Instagram and Snapchat (Aschbrenner et al. 2019 ).

Research has also explored the motivations for using social media as well as the perceived benefits of interacting on these platforms among individuals with mental illness. In the sections that follow (see Table 1 for a summary), we consider three potentially unique features of interacting and connecting with others on social media that may offer benefits for individuals living with mental illness. These include: (1) Facilitate social interaction; (2) Access to a peer support network; and (3) Promote engagement and retention in services.

Facilitate Social Interaction

Social media platforms offer near continuous opportunities to connect and interact with others, regardless of time of day or geographic location. This on demand ease of communication may be especially important for facilitating social interaction among individuals with mental disorders experiencing difficulties interacting in face-to-face settings. For example, impaired social functioning is a common deficit in schizophrenia spectrum disorders, and social media may facilitate communication and interacting with others for these individuals (Torous and Keshavan 2016 ). This was suggested in one study where participants with schizophrenia indicated that social media helped them to interact and socialize more easily (Miller et al. 2015 ). Like other online communication, the ability to connect with others anonymously may be an important feature of social media, especially for individuals living with highly stigmatizing health conditions (Berger et al. 2005 ), such as serious mental disorders (Highton-Williamson et al. 2015 ).

Studies have found that individuals with serious mental disorders (Spinzy et al. 2012 ) as well as young adults with mental illness (Gowen et al. 2012 ) appear to form online relationships and connect with others on social media as often as social media users from the general population. This is an important observation because individuals living with serious mental disorders typically have few social contacts in the offline world and also experience high rates of loneliness (Badcock et al. 2015 ; Giacco et al. 2016 ). Among individuals receiving publicly funded mental health services who use social media, nearly half (47%) reported using these platforms at least weekly to feel less alone (Brusilovskiy et al. 2016 ). In another study of young adults with serious mental illness, most indicated that they used social media to help feel less isolated (Gowen et al. 2012 ). Interestingly, more frequent use of social media among a sample of individuals with serious mental illness was associated with greater community participation, measured as participation in shopping, work, religious activities, or visiting friends and family, as well as greater civic engagement, reflected as voting in local elections (Brusilovskiy et al. 2016 ).

Emerging research also shows that young people with moderate to severe depressive symptoms appear to prefer communicating on social media rather than in-person (Rideout and Fox 2018 ), while other studies have found that some individuals may prefer to seek help for mental health concerns online rather than through in-person encounters (Batterham and Calear 2017 ). In a qualitative study, participants with schizophrenia described greater anonymity, the ability to discover that other people have experienced similar health challenges and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information (Schrank et al. 2010 ). Because social media does not require the immediate responses necessary in face-to-face communication, it may overcome deficits with social interaction due to psychotic symptoms that typically adversely affect face-to-face conversations (Docherty et al. 1996 ). Online social interactions may not require the use of non-verbal cues, particularly in the initial stages of interaction (Kiesler et al. 1984 ), with interactions being more fluid and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction (Indian and Grieve 2014 ). Furthermore, many individuals with serious mental disorders can experience symptoms including passive social withdrawal, blunted affect, and attentional impairment, as well as active social avoidance due to hallucinations or other concerns (Hansen et al. 2009 ), thus potentially reinforcing the relative advantage, as perceived by users, of using social media over in person conversations.

Access to a Peer Support Network

There is growing recognition about the role that social media channels could play in enabling peer support (Bucci et al. 2019 ; Naslund et al. 2016b ), referred to as a system of mutual giving and receiving where individuals who have endured the difficulties of mental illness can offer hope, friendship, and support to others facing similar challenges (Davidson et al. 2006 ; Mead et al. 2001 ). Initial studies exploring use of online self-help forums among individuals with serious mental illnesses have found that individuals with schizophrenia appeared to use these forums for self-disclosure and sharing personal experiences, in addition to providing or requesting information, describing symptoms, or discussing medication (Haker et al. 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness (Vayreda and Antaki 2009 ). More recently, in a review of online social networking in people with psychosis, Highton-Williamson et al. ( 2015 ) highlight that an important purpose of such online connections was to establish new friendships, pursue romantic relationships, maintain existing relationships or reconnect with people, and seek online peer support from others with lived experience (Highton-Williamson et al. 2015 ).

Online peer support among individuals with mental illness has been further elaborated in various studies. In a content analysis of comments posted to YouTube by individuals who self-identified as having a serious mental illness, there appeared to be opportunities to feel less alone, provide hope, find support and learn through mutual reciprocity, and share coping strategies for day-to-day challenges of living with a mental illness (Naslund et al. 2014 ). In another study, Chang ( 2009 ) delineated various communication patterns in an online psychosis peer-support group (Chang 2009 ). Specifically, different forms of support emerged, including “informational support” about medication use or contacting mental health providers, “esteem support” involving positive comments for encouragement, “network support” for sharing similar experiences, and “emotional support” to express understanding of a peer’s situation and offer hope or confidence (Chang 2009 ). Bauer et al. ( 2013 ) reported that the main interest in online self-help forums for patients with bipolar disorder was to share emotions with others, allow exchange of information, and benefit by being part of an online social group (Bauer et al. 2013 ).

For individuals who openly discuss mental health problems on Twitter, a study by Berry et al. ( 2017 ) found that this served as an important opportunity to seek support and to hear about the experiences of others (Berry et al. 2017 ). In a survey of social media users with mental illness, respondents reported that sharing personal experiences about living with mental illness and opportunities to learn about strategies for coping with mental illness from others were important reasons for using social media (Naslund et al. 2017 ). A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared (Saha et al. 2019 ). Taken together, these studies offer insights about the potential for social media to facilitate access to an informal peer support network, though more research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts.

Promote Engagement and Retention in Services

Many individuals living with mental disorders have expressed interest in using social media platforms for seeking mental health information (Lal et al. 2018 ), connecting with mental health providers (Birnbaum et al. 2017b ), and accessing evidence-based mental health services delivered over social media specifically for coping with mental health symptoms or for promoting overall health and wellbeing (Naslund et al. 2017 ). With the widespread use of social media among individuals living with mental illness combined with the potential to facilitate social interaction and connect with supportive peers, as summarized above, it may be possible to leverage the popular features of social media to enhance existing mental health programs and services. A recent review by Biagianti et al. ( 2018 ) found that peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions and may also improve perceived social support (Biagianti et al. 2018 ).

Among digital programs that have incorporated peer-to-peer social networking consistent with popular features on social media platforms, a pilot study of the HORYZONS online psychosocial intervention demonstrated significant reductions in depression among patients with first episode psychosis (Alvarez-Jimenez et al. 2013 ). Importantly, the majority of participants (95%) in this study engaged with the peer-to-peer networking feature of the program, with many reporting increases in perceived social connectedness and empowerment in their recovery process (Alvarez-Jimenez et al. 2013 ). This moderated online social therapy program is now being evaluated as part of a large randomized controlled trial for maintaining treatment effects from first episode psychosis services (Alvarez-Jimenez et al. 2019 ).

Other early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis (Alvarez-Jimenez et al. 2018 ). There has also been a recent emergence of several mobile apps to support symptom monitoring and relapse prevention in psychotic disorders. Among these apps, the development of PRIME (Personalized Real-time Intervention for Motivational Enhancement) has involved working closely with young people with schizophrenia to ensure that the design of the app has the look and feel of mainstream social media platforms, as opposed to existing clinical tools (Schlosser et al. 2016 ). This unique approach to the design of the app is aimed at promoting engagement and ensuring that the app can effectively improve motivation and functioning through goal setting and promoting better quality of life of users with schizophrenia (Schlosser et al. 2018 ).

Social media platforms could also be used to promote engagement and participation in in-person services delivered through community mental health settings. For example, the peer-based lifestyle intervention called PeerFIT targets weight loss and improved fitness among individuals living with serious mental illness through a combination of in-person lifestyle classes, exercise groups, and use of digital technologies (Aschbrenner et al. 2016b , c ). The intervention holds tremendous promise as lack of support is one of the largest barriers towards exercise in patients with serious mental illness (Firth et al. 2016 ), and it is now possible to use social media to counter such. Specifically, in PeerFIT, a private Facebook group is closely integrated into the program to offer a closed platform where participants can connect with the lifestyle coaches, access intervention content, and support or encourage each other as they work towards their lifestyle goals (Aschbrenner et al. 2016a ; Naslund et al. 2016a ). To date, this program has demonstrated preliminary effectiveness for meaningfully reducing cardiovascular risk factors that contribute to early mortality in this patient group (Aschbrenner, Naslund, Shevenell, Kinney, et al., 2016), while the Facebook component appears to have increased engagement in the program, while allowing participants who were unable to attend in-person sessions due to other health concerns or competing demands to remain connected with the program (Naslund et al. 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from real world community mental health services settings (Aschbrenner et al. 2018a ).

These examples highlight the promise of incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes. This is an emerging area of research, as evidenced by several important effectiveness trials underway (Alvarez-Jimenez et al. 2019 ; Aschbrenner et al. 2018a ), including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services (Gleeson et al. 2017 ).

Challenges with Social Media for Mental Health

The science on the role of social media for engaging persons with mental disorders needs a cautionary note on the effects of social media usage on mental health and wellbeing, particularly in adolescents and young adults. While the risks and harms of social media are frequently covered in the popular press and mainstream news reports, careful consideration of the research in this area is necessary. In a review of 43 studies in young people, many benefits of social media were cited, including increased self-esteem and opportunities for self-disclosure (Best et al. 2014 ). Yet, reported negative effects were an increased exposure to harm, social isolation, depressive symptoms, and bullying (Best et al. 2014 ). In the sections that follow (see Table 1 for a summary), we consider three major categories of risk related to use of social media and mental health. These include: (1) Impact on symptoms; (2) Facing hostile interactions; and (3) Consequences for daily life.

Impact on Symptoms

Studies consistently highlight that use of social media, especially heavy use and prolonged time spent on social media platforms, appears to contribute to increased risk for a variety of mental health symptoms and poor wellbeing, especially among young people (Andreassen et al. 2016 ; Kross et al. 2013 ; Woods and Scott 2016 ). This may partly be driven by the detrimental effects of screen time on mental health, including increased severity of anxiety and depressive symptoms, which have been well documented (Stiglic and Viner 2019 ). Recent studies have reported negative effects of social media use on mental health of young people, including social comparison pressure with others and greater feeling of social isolation after being rejected by others on social media (Rideout and Fox 2018 ). In a study of young adults, it was found that negative comparisons with others on Facebook contributed to risk of rumination and subsequent increases in depression symptoms (Feinstein et al. 2013 ). Still, the cross-sectional nature of many screen time and mental health studies makes it challenging to reach causal inferences (Orben and Przybylski 2019 ).

Quantity of social media use is also an important factor, as highlighted in a survey of young adults ages 19 to 32, where more frequent visits to social media platforms each week were correlated with greater depressive symptoms (Lin et al. 2016 ). More time spent using social media is also associated with greater symptoms of anxiety (Vannucci et al. 2017 ). The actual number of platforms accessed also appears to contribute to risk as reflected in another national survey of young adults where use of a large number of social media platforms was associated with negative impact on mental health (Primack et al. 2017 ). Among survey respondents using between 7 and 11 different social media platforms compared with respondents using only 2 or fewer platforms, there were 3 times greater odds of having high levels of depressive symptoms and a 3.2 times greater odds of having high levels of anxiety symptoms (Primack et al. 2017 ).

Many researchers have postulated that worsening mental health attributed to social media use may be because social media replaces face-to-face interactions for young people (Twenge and Campbell 2018 ) and may contribute to greater loneliness (Bucci et al. 2019 ) and negative effects on other aspects of health and wellbeing (Woods and Scott 2016 ). One nationally representative survey of US adolescents found that among respondents who reported more time accessing media such as social media platforms or smartphone devices, there were significantly greater depressive symptoms and increased risk of suicide when compared with adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities (Twenge et al. 2018 ). For individuals living with more severe mental illnesses, the effects of social media on psychiatric symptoms have received less attention. One study found that participation in chat rooms may contribute to worsening symptoms in young people with psychotic disorders (Mittal et al. 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood (Berry et al. 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies (Orben and Przybylski 2019 ) and emphasize the need to explore how social media may contribute to symptom severity and whether protective factors may be identified to mitigate these risks.

Facing Hostile Interactions

Popular social media platforms can create potential situations where individuals may be victimized by negative comments or posts. Cyberbullying represents a form of online aggression directed towards specific individuals, such as peers or acquaintances, which is perceived to be most harmful when compared with random hostile comments posted online (Hamm et al. 2015 ). Importantly, cyberbullying on social media consistently shows harmful impact on mental health in the form of increased depressive symptoms as well as worsening of anxiety symptoms, as evidenced in a review of 36 studies among children and young people (Hamm et al. 2015 ). Furthermore, cyberbullying disproportionately impacts females as reflected in a national survey of adolescents in the USA, where females were twice as likely to be victims of cyberbullying compared with males (Alhajji et al. 2019 ). Most studies report cross-sectional associations between cyberbullying and symptoms of depression or anxiety (Hamm et al. 2015 ), though one longitudinal study in Switzerland found that cyberbullying contributed to significantly greater depression over time (Machmutow et al. 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there were over 3 times greater odds of facing online harassment in the last year compared with youth who reported mild or no depressive symptoms (Ybarra 2004 ). Similarly, in a 2018 national survey of young people, respondents ages 14 to 22 with moderate to severe depressive symptoms were more likely to have had negative experiences when using social media and, in particular, were more likely to report having faced hostile comments or being “trolled” from others when compared with respondents without depressive symptoms (31% vs. 14%) (Rideout and Fox 2018 ). As these studies depict risks for victimization on social media and the correlation with poor mental health, it is possible that individuals living with mental illness may also experience greater hostility online compared to individuals without mental illness. This would be consistent with research showing greater risk of hostility, including increased violence and discrimination, directed towards individuals living with mental illness in in-person contexts, especially targeted at those with severe mental illnesses (Goodman et al. 1999 ).

A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media (Saha et al. 2019 ). Another study was able to map the spread of social media posts about the Blue Whale Challenge, an alleged game promoting suicide, over Twitter, YouTube, Reddit, Tumblr, and other forums across 127 countries (Sumner et al. 2019 ). These findings show that it is critical to monitor the actual content of social media posts, such as determining whether content is hostile or promotes harm to self or others. This is pertinent because existing research looking at duration of exposure cannot account for the impact of specific types of content on mental health and is insufficient to fully understand the effects of using these platforms on mental health.

Consequences for Daily Life

The ways in which individuals use social media can also impact their offline relationships and everyday activities. To date, reports have described risks of social media use pertaining to privacy, confidentiality, and unintended consequences of disclosing personal health information online (Torous and Keshavan 2016 ). Additionally, concerns have been raised about poor quality or misleading health information shared on social media and that social media users may not be aware of misleading information or conflicts of interest especially when the platforms promote popular content regardless of whether it is from a trustworthy source (Moorhead et al. 2013 ; Ventola 2014 ). For persons living with mental illness, there may be additional risks from using social media. A recent study that specifically explored the perspectives of social media users with serious mental illnesses, including participants with schizophrenia spectrum disorders, bipolar disorder, or major depression, found that over one third of participants expressed concerns about privacy when using social media (Naslund and Aschbrenner 2019 ). The reported risks of social media use were directly related to many aspects of everyday life, including concerns about threats to employment, fear of stigma and being judged, impact on personal relationships, and facing hostility or being hurt (Naslund and Aschbrenner 2019 ). While few studies have specifically explored the dangers of social media use from the perspectives of individuals living with mental illness, it is important to recognize that use of these platforms may contribute to risks that extend beyond worsening symptoms and that can affect different aspects of daily life.

In this commentary, we considered ways in which social media may yield benefits for individuals living with mental illness, while contrasting these with the possible harms. Studies reporting on the threats of social media for individuals with mental illness are mostly cross-sectional, making it difficult to draw conclusions about direction of causation. However, the risks are potentially serious. These risks should be carefully considered in discussions pertaining to use of social media and the broader use of digital mental health technologies, as avenues for mental health promotion or for supporting access to evidence-based programs or mental health services. At this point, it would be premature to view the benefits of social media as outweighing the possible harms, when it is clear from the studies summarized here that social media use can have negative effects on mental health symptoms, can potentially expose individuals to hurtful content and hostile interactions, and can result in serious consequences for daily life, including threats to employment and personal relationships. Despite these risks, it is also necessary to recognize that individuals with mental illness will continue to use social media given the ease of accessing these platforms and the immense popularity of online social networking. With this in mind, it may be ideal to raise awareness about these possible risks so that individuals can implement necessary safeguards, while highlighting that there could also be benefits. Being aware of the risks is an essential first step, before then recognizing that use of these popular platforms could contribute to some benefits like finding meaningful interactions with others, engaging with peer support networks, and accessing information and services.

To capitalize on the widespread use of social media and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental illness use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm. It will be important to recognize how gender and race contribute to differences in use of social media for seeking mental health information or accessing interventions, as well as differences in how social media might impact mental wellbeing. For example, a national survey of 14- to 22-year olds in the USA found that female respondents were more likely to search online for information about depression or anxiety and to try to connect with other people online who share similar mental health concerns when compared with male respondents (Rideout and Fox 2018 ). In the same survey, there did not appear to be any differences between racial or ethnic groups in social media use for seeking mental health information (Rideout and Fox 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males (Booker et al. 2018 ), highlighting the need to explore unique experiences between gender groups to inform tailored programs and services. Research shows that lesbian, gay, bisexual, or transgender individuals frequently use social media for searching for health information and may be more likely compared with heterosexual individuals to share their own personal health experiences with others online (Rideout and Fox 2018 ). Less is known about use of social media for seeking support for mental health concerns among gender minorities, though this is an important area for further investigation as these individuals are more likely to experience mental health problems and online victimization when compared with heterosexual individuals (Mereish et al. 2019 ).

Similarly, efforts are needed to explore the relationship between social media use and mental health among ethnic and racial minorities. A recent study found that exposure to traumatic online content on social media showing violence or hateful posts directed at racial minorities contributed to increases in psychological distress, PTSD symptoms, and depression among African American and Latinx adolescents in the USA (Tynes et al. 2019 ). These concerns are contrasted by growing interest in the potential for new technologies including social media to expand the reach of services to underrepresented minority groups (Schueller et al. 2019 ). Therefore, greater attention is needed to understanding the perspectives of ethnic and racial minorities to inform effective and safe use of social media for mental health promotion efforts.

Research has found that individuals living with mental illness have expressed interest in accessing mental health services through social media platforms. A survey of social media users with mental illness found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system (Naslund et al. 2017 ). Importantly, individuals with serious mental illness have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.

An important strength with this commentary is that it combines a range of studies broadly covering the topic of social media and mental health. We have provided a summary of recent evidence in a rapidly advancing field with the goal of presenting unique ways that social media could offer benefits for individuals with mental illness, while also acknowledging the potentially serious risks and the need for further investigation. There are also several limitations with this commentary that warrant consideration. Importantly, as we aimed to address this broad objective, we did not conduct a systematic review of the literature. Therefore, the studies reported here are not exhaustive, and there may be additional relevant studies that were not included. Additionally, we only summarized published studies, and as a result, any reports from the private sector or websites from different organizations using social media or other apps containing social media–like features would have been omitted. Although, it is difficult to rigorously summarize work from the private sector, sometimes referred to as “gray literature,” because many of these projects are unpublished and are likely selective in their reporting of findings given the target audience may be shareholders or consumers.

Another notable limitation is that we did not assess risk of bias in the studies summarized in this commentary. We found many studies that highlighted risks associated with social media use for individuals living with mental illness; however, few studies of programs or interventions reported negative findings, suggesting the possibility that negative findings may go unpublished. This concern highlights the need for a future more rigorous review of the literature with careful consideration of bias and an accompanying quality assessment. Most of the studies that we described were from the USA, as well as from other higher income settings such as Australia or the UK. Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide (Naslund et al. 2019 ).

Future Directions for Social Media and Mental Health

As we consider future research directions, the near ubiquitous social media use also yields new opportunities to study the onset and manifestation of mental health symptoms and illness severity earlier than traditional clinical assessments. There is an emerging field of research referred to as “digital phenotyping” aimed at capturing how individuals interact with their digital devices, including social media platforms, in order to study patterns of illness and identify optimal time points for intervention (Jain et al. 2015 ; Onnela and Rauch 2016 ). Given that most people access social media via mobile devices, digital phenotyping and social media are closely related (Torous et al. 2019 ). To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms (Shatte et al. 2019 ), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health (Manikonda and De Choudhury 2017 ; Reece et al. 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression (De Choudhury et al. 2013 ) as well as detecting users’ mood and affective states (De Choudhury et al. 2012 ), while photos posted to Instagram can yield insights for predicting depression (Reece and Danforth 2017 ). The intersection of social media and digital phenotyping will likely add new levels of context to social media use in the near future.

Several studies have also demonstrated that when compared with a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns (Birnbaum et al. 2017a ), including more frequent discussion of tobacco use (Hswen et al. 2017 ), symptoms of depression and anxiety (Hswen et al. 2018b ), and suicide (Hswen et al. 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala et al. 2017 ). Each of these examples offers early insights into the potential to leverage widely available online data for better understanding the onset and course of mental illness. It is possible that social media data could be used to supplement additional digital data, such as continuous monitoring using smartphone apps or smart watches, to generate a more comprehensive “digital phenotype” to predict relapse and identify high-risk health behaviors among individuals living with mental illness (Torous et al. 2019 ).

With research increasingly showing the valuable insights that social media data can yield about mental health states, greater attention to the ethical concerns with using individual data in this way is necessary (Chancellor et al. 2019 ). For instance, data is typically captured from social media platforms without the consent or awareness of users (Bidargaddi et al. 2017 ), which is especially crucial when the data relates to a socially stigmatizing health condition such as mental illness (Guntuku et al. 2017 ). Precautions are needed to ensure that data is not made identifiable in ways that were not originally intended by the user who posted the content as this could place an individual at risk of harm or divulge sensitive health information (Webb et al. 2017 ; Williams et al. 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, and the target individuals with mental illness throughout the collection of data, development of predictive algorithms, and interpretation of findings (Chancellor et al. 2019 ).

In recognizing that many individuals living with mental illness use social media to search for information about their mental health, it is possible that they may also want to ask their clinicians about what they find online to check if the information is reliable and trustworthy. Alternatively, many individuals may feel embarrassed or reluctant to talk to their clinicians about using social media to find mental health information out of concerns of being judged or dismissed. Therefore, mental health clinicians may be ideally positioned to talk with their patients about using social media and offer recommendations to promote safe use of these sites while also respecting their patients’ autonomy and personal motivations for using these popular platforms. Given the gap in clinical knowledge about the impact of social media on mental health, clinicians should be aware of the many potential risks so that they can inform their patients while remaining open to the possibility that their patients may also experience benefits through use of these platforms. As awareness of these risks grows, it may be possible that new protections will be put in place by industry or through new policies that will make the social media environment safer. It is hard to estimate a number needed to treat or harm today given the nascent state of research, which means the patient and clinician need to weigh the choice on a personal level. Thus, offering education and information is an important first step in that process. As patients increasingly show interest in accessing mental health information or services through social media, it will be necessary for health systems to recognize social media as a potential avenue for reaching or offering support to patients. This aligns with growing emphasis on the need for greater integration of digital psychiatry, including apps, smartphones, or wearable devices, into patient care and clinical services through institution-wide initiatives and training clinical providers (Hilty et al. 2019 ). Within a learning healthcare environment where research and care are tightly intertwined and feedback between both is rapid, the integration of digital technologies into services may create new opportunities for advancing use of social media for mental health.

As highlighted in this commentary, social media has become an important part of the lives of many individuals living with mental disorders. Many of these individuals use social media to share their lived experiences with mental illness, to seek support from others, and to search for information about treatment recommendations, accessing mental health services and coping with symptoms (Bucci et al. 2019 ; Highton-Williamson et al. 2015 ; Naslund et al. 2016b ). As the field of digital mental health advances, the wide reach, ease of access, and popularity of social media platforms could be used to allow individuals in need of mental health services or facing challenges of mental illness to access evidence-based treatment and support. To achieve this end and to explore whether social media platforms can advance efforts to close the gap in available mental health services in the USA and globally, it will be essential for researchers to work closely with clinicians and with those affected by mental illness to ensure that possible benefits of using social media are carefully weighed against anticipated risks.

Abdel-Baki, A., Lal, S., Charron, D.-C., Stip, E., & Kara, N. (2017). Understanding access and use of technology among youth with first-episode psychosis to inform the development of technology-enabled therapeutic interventions. Early Intervention in Psychiatry, 11 (1), 72–76.

PubMed   Google Scholar  

Ahmed, Y. A., Ahmad, M. N., Ahmad, N., & Zakaria, N. H. (2019). Social media for knowledge-sharing: a systematic literature review. Telematics and Informatics, 37 , 72–112.

Google Scholar  

Alhajji, M., Bass, S., & Dai, T. (2019). Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 youth risk behavior survey. Global Pediatric Health, 6 , 2333794X19868887.

PubMed   PubMed Central   Google Scholar  

Alvarez-Jimenez, M., Bendall, S., Lederman, R., Wadley, G., Chinnery, G., Vargas, S., Larkin, M., Killackey, E., McGorry, P., & Gleeson, J. F. (2013). On the HORYZON: moderated online social therapy for long-term recovery in first episode psychosis. Schizophrenia Research, 143 (1), 143–149.

Alvarez-Jimenez, M., Gleeson, J., Bendall, S., Penn, D., Yung, A., Ryan, R., et al. (2018). Enhancing social functioning in young people at ultra high risk (UHR) for psychosis: a pilot study of a novel strengths and mindfulness-based online social therapy. Schizophrenia Research, 202 , 369–377.

Alvarez-Jimenez, M., Bendall, S., Koval, P., Rice, S., Cagliarini, D., Valentine, L., et al. (2019). HORYZONS trial: protocol for a randomised controlled trial of a moderated online social therapy to maintain treatment effects from first-episode psychosis services. BMJ Open, 9 (2), e024104.

Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychology of Addictive Behaviors, 30 (2), 252.

Aschbrenner, K. A., Naslund, J. A., & Bartels, S. J. (2016a). A mixed methods study of peer-to-peer support in a group-based lifestyle intervention for adults with serious mental illness. Psychiatric Rehabilitation Journal, 39 (4), 328–334.

Aschbrenner, K. A., Naslund, J. A., Shevenell, M., Kinney, E., & Bartels, S. J. (2016b). A pilot study of a peer-group lifestyle intervention enhanced with mHealth technology and social media for adults with serious mental illness. The Journal of Nervous and Mental Disease, 204 (6), 483–486.

Aschbrenner, K. A., Naslund, J. A., Shevenell, M., Mueser, K. T., & Bartels, S. J. (2016c). Feasibility of behavioral weight loss treatment enhanced with peer support and mobile health technology for individuals with serious mental illness. Psychiatric Quarterly, 87 (3), 401–415.

Aschbrenner, K. A., Naslund, J. A., Gorin, A. A., Mueser, K. T., Scherer, E. A., Viron, M., et al. (2018a). Peer support and mobile health technology targeting obesity-related cardiovascular risk in young adults with serious mental illness: protocol for a randomized controlled trial. Contemporary Clinical Trials, 74 , 97–106.

Aschbrenner, K. A., Naslund, J. A., Grinley, T., Bienvenida, J. C. M., Bartels, S. J., & Brunette, M. (2018b). A survey of online and mobile technology use at peer support agencies. Psychiatric Quarterly , 1–10.

Aschbrenner, K. A., Naslund, J. A., Tomlinson, E. F., Kinney, A., Pratt, S. I., & Brunette, M. F. (2019). Adolescents’ use of digital technologies and preferences for mobile health coaching in mental health settings. Frontiers in Public Health. 7 , 178.

Badcock, J. C., Shah, S., Mackinnon, A., Stain, H. J., Galletly, C., Jablensky, A., & Morgan, V. A. (2015). Loneliness in psychotic disorders and its association with cognitive function and symptom profile. Schizophrenia Research, 169 (1–3), 268–273.

Batterham, P. J., & Calear, A. J. (2017). Preferences for internet-based mental health interventions in an adult online sample: findings from ann online community survey. JMIR Mental Health, 4 (2), e26.

Bauer, R., Bauer, M., Spiessl, H., & Kagerbauer, T. (2013). Cyber-support: an analysis of online self-help forums (online self-help forums in bipolar disorder). Nordic Journal of Psychiatry, 67 (3), 185–190.

Berger, M., Wagner, T. H., & Baker, L. C. (2005). Internet use and stigmatized illness. Social Science & Medicine, 61 (8), 1821–1827.

Berry, N., Lobban, F., Belousov, M., Emsley, R., Nenadic, G., & Bucci, S. (2017). # WhyWeTweetMH: understanding why people use Twitter to discuss mental health problems. Journal of Medical Internet Research, 19 (4), e107.

Berry, N., Emsley, R., Lobban, F., & Bucci, S. (2018). Social media and its relationship with mood, self-esteem and paranoia in psychosis. Acta Psychiatrica Scandinavica, 138 , 558–570.

Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing: a systematic narrative review. Children and Youth Services Review, 41 , 27–36.

Biagianti, B., Quraishi, S. H., & Schlosser, D. A. (2018). Potential benefits of incorporating peer-to-peer interactions into digital interventions for psychotic disorders: a systematic review. Psychiatric Services, 69 (4), 377–388.

Bidargaddi, N., Musiat, P., Makinen, V.-P., Ermes, M., Schrader, G., & Licinio, J. (2017). Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Molecular Psychiatry, 22 (2), 164.

Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., De Choudhury, M., & Kane, J. M. (2017a). A collaborative approach to identifying social media markers of schizophrenia by employing machine learning and clinical appraisals. Journal of Medical Internet Research, 19 (8), e289.

Birnbaum, M. L., Rizvi, A. F., Correll, C. U., Kane, J. M., & Confino, J. (2017b). Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders. Early Intervention in Psychiatry, 11 (4), 290–295.

Booker, C. L., Kelly, Y. J., & Sacker, A. (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK. BMC Public Health, 18 (1), 321.

Brunette, M., Achtyes, E., Pratt, S., Stilwell, K., Opperman, M., Guarino, S., & Kay-Lambkin, F. (2019). Use of smartphones, computers and social media among people with SMI: opportunity for intervention. Community Mental Health Journal , 1–6.

Brusilovskiy, E., Townley, G., Snethen, G., & Salzer, M. S. (2016). Social media use, community participation and psychological well-being among individuals with serious mental illnesses. Computers in Human Behavior, 65 , 232–240.

Bucci, S., Schwannauer, M., & Berry, N. (2019). The digital revolution and its impact on mental health care. Psychology and Psychotherapy: Theory, Research and Practice, 92 (2), 277–297.

Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M., & De Choudhury, M. (2019). A taxonomy of ethical tensions in inferring mental health states from social media. In Proceedings of the Conference on Fairness, Accountability, and Transparency, 79–88.

Chang, H. J. (2009). Online supportive interactions: using a network approach to examine communication patterns within a psychosis social support group in Taiwan. Journal of the American Society for Information Science and Technology, 60 (7), 1504–1517.

Davidson, L., Chinman, M., Sells, D., & Rowe, M. (2006). Peer support among adults with serious mental illness: a report from the field. Schizophrenia Bulletin, 32 (3), 443–450.

De Choudhury, M., Gamon, M., & Counts, S. (2012). Happy, nervous or surprised? classification of human affective states in social media. Paper presented at the sixth international Association for Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Meedia, 435–438.

De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. Paper presented at the seventh international Association for Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Media, 128–137.

Docherty, N. M., Hawkins, K. A., Hoffman, R. E., Quinlan, D. M., Rakfeldt, J., & Sledge, W. H. (1996). Working memory, attention, and communication disturbances in schizophrenia. Journal of Abnormal Psychology, 105 (2), 212–219.

Ernala, S. K., Rizvi, A. F., Birnbaum, M. L., Kane, J. M., & De Choudhury, M. (2017). Linguistic markers indicating therapeutic outcomes of social media disclosures of schizophrenia. Proceedings of the ACM on Human-Computer Interaction, 1 (1), 43.

Feinstein, B. A., Hershenberg, R., Bhatia, V., Latack, J. A., Meuwly, N., & Davila, J. (2013). Negative social comparison on Facebook and depressive symptoms: rumination as a mechanism. Psychology of Popular Media Culture, 2 (3), 161.

Firth, J., Cotter, J., Torous, J., Bucci, S., Firth, J. A., & Yung, A. R. (2015). Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies. Schizophrenia Bulletin, 42 (2), 448–455.

Firth, J., Rosenbaum, S., Stubbs, B., Gorczynski, P., Yung, A. R., & Vancampfort, D. (2016). Motivating factors and barriers towards exercise in severe mental illness: a systematic review and meta-analysis. Psychological Medicine, 46 (14), 2869–2881.

Gay, K., Torous, J., Joseph, A., Pandya, A., & Duckworth, K. (2016). Digital technology use among individuals with schizophrenia: results of an online survey. JMIR Mental Health, 3 (2), e15.

Giacco, D., Palumbo, C., Strappelli, N., Catapano, F., & Priebe, S. (2016). Social contacts and loneliness in people with psychotic and mood disorders. Comprehensive Psychiatry, 66 , 59–66.

Gleeson, J., Lederman, R., Herrman, H., Koval, P., Eleftheriadis, D., Bendall, S., Cotton, S. M., & Alvarez-Jimenez, M. (2017). Moderated online social therapy for carers of young people recovering from first-episode psychosis: study protocol for a randomised controlled trial. Trials, 18 (1), 27.

Glick, G., Druss, B., Pina, J., Lally, C., & Conde, M. (2016). Use of mobile technology in a community mental health setting. Journal of Telemedicine and Telecare, 22 (7), 430–435.

Goodman, L. A., Thompson, K. M., Weinfurt, K., Corl, S., Acker, P., Mueser, K. T., & Rosenberg, S. D. (1999). Reliability of reports of violent victimization and posttraumatic stress disorder among men and women with serious mental illness. Journal of Traumatic Stress: Official Publication of the International Society for Traumatic Stress Studies, 12 (4), 587–599.

Gowen, K., Deschaine, M., Gruttadara, D., & Markey, D. (2012). Young adults with mental health conditions and social networking websites: seeking tools to build community. Psychiatric Rehabilitation Journal, 35 (3), 245–250.

Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18 , 43–49.

Haker, H., Lauber, C., & Rössler, W. (2005). Internet forums: a self-help approach for individuals with schizophrenia? Acta Psychiatrica Scandinavica, 112 (6), 474–477.

Hamm, M. P., Newton, A. S., Chisholm, A., Shulhan, J., Milne, A., Sundar, P., Ennis, H., Scott, S. D., & Hartling, L. (2015). Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatrics, 169 (8), 770–777.

Hansen, C. F., Torgalsbøen, A.-K., Melle, I., & Bell, M. D. (2009). Passive/apathetic social withdrawal and active social avoidance in schizophrenia: difference in underlying psychological processes. The Journal of Nervous and Mental Disease, 197 (4), 274–277.

Highton-Williamson, E., Priebe, S., & Giacco, D. (2015). Online social networking in people with psychosis: a systematic review. International Journal of Social Psychiatry, 61 (1), 92–101.

Hilty, D. M., Chan, S., Torous, J., Luo, J., & Boland, R. J. (2019). Mobile health, smartphone/device, and apps for psychiatry and medicine: competencies, training, and faculty development issues. Psychiatric Clinics, 42 (3), 513–534.

Hswen, Y., Naslund, J. A., Chandrashekar, P., Siegel, R., Brownstein, J. S., & Hawkins, J. B. (2017). Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia. Psychiatry Research, 257 , 479–484.

Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018a). Monitoring online discussions about suicide among Twitter users with schizophrenia: exploratory study. JMIR Mental Health, 5 (4), e11483.

Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018b). Online communication about depression and anxiety among twitter users with schizophrenia: preliminary findings to inform a digital phenotype using social media. Psychiatric Quarterly, 89 (3), 569–580.

Indian, M., & Grieve, R. (2014). When Facebook is easier than face-to-face: social support derived from Facebook in socially anxious individuals. Personality and Individual Differences, 59 , 102–106.

Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015). The digital phenotype. Nature Biotechnology, 33 (5), 462–463.

Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-mediated communication. American Psychologist, 39 , 1123–1134.

Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., Shablack, H., Jonides, J., & Ybarra, O. (2013). Facebook use predicts declines in subjective well-being in young adults. PLoS One, 8 (8), e69841.

Lal, S., Nguyen, V., & Theriault, J. (2018). Seeking mental health information and support online: experiences and perspectives of young people receiving treatment for first-episode psychosis. Early Intervention in Psychiatry, 12 (3), 324–330.

Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., & Primack, B. A. (2016). Association between social media use and depression among US young adults. Depression and Anxiety, 33 (4), 323–331.

Machmutow, K., Perren, S., Sticca, F., & Alsaker, F. D. (2012). Peer victimisation and depressive symptoms: can specific coping strategies buffer the negative impact of cybervictimisation? Emotional and Behavioural Difficulties, 17 (3–4), 403–420.

Manikonda, L., & De Choudhury, M. (2017). Modeling and understanding visual attributes of mental health disclosures in social media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 170–181.

Mead, S., Hilton, D., & Curtis, L. (2001). Peer support: a theoretical perspective. Psychiatric Rehabilitation Journal, 25 (2), 134–141.

Mereish, E. H., Sheskier, M., Hawthorne, D. J., & Goldbach, J. T. (2019). Sexual orientation disparities in mental health and substance use among Black American young people in the USA: effects of cyber and bias-based victimisation. Culture, Health & Sexuality, 21 (9), 985–998.

Miller, B. J., Stewart, A., Schrimsher, J., Peeples, D., & Buckley, P. F. (2015). How connected are people with schizophrenia? Cell phone, computer, email, and social media use. Psychiatry Research, 225 (3), 458–463.

Mittal, V. A., Tessner, K. D., & Walker, E. F. (2007). Elevated social Internet use and schizotypal personality disorder in adolescents. Schizophrenia Research, 94 (1–3), 50–57.

Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15 (4), e85.

Naslund, J. A., & Aschbrenner, K. A. (2019). Risks to privacy with use of social media: understanding the views of social media users with serious mental illness. Psychiatric Services, 70 (7), 561–568.

Naslund, J. A., Grande, S. W., Aschbrenner, K. A., & Elwyn, G. (2014). Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube. PLoS One, 9 (10), e110171.

Naslund, J. A., Aschbrenner, K. A., & Bartels, S. J. (2016). How people living with serious mental illness use smartphones, mobile apps, and social media. Psychiatric Rehabilitation Journal, 39 (4), 364–367.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016a). Feasibility and acceptability of Facebook for health promotion among people with serious mental illness. Digital Health, 2 , 2055207616654822.

PubMed Central   Google Scholar  

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016b). The future of mental health care: peer-to-peer support and social media. Epidemiology and Psychiatric Sciences, 25 (2), 113–122.

Naslund, J. A., Aschbrenner, K. A., McHugo, G. J., Unützer, J., Marsch, L. A., & Bartels, S. J. (2019). Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness. Early Intervention in Psychiatry, 13 (3), 405–413.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., McHugo, G. J., & Bartels, S. J. (2018). Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: an exploratory study. Psychiatric Quarterly, 89 (1), 81–94.

Naslund, J. A., Gonsalves, P. P., Gruebner, O., Pendse, S. R., Smith, S. L., Sharma, A., & Raviola, G. (2019). Digital innovations for global mental health: opportunities for data science, task sharing, and early intervention. Current Treatment Options in Psychiatry , 1–15.

Onnela, J.-P., & Rauch, S. L. (2016). Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology, 41 (7), 1691–1696.

Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3 (2), 173–182.

Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., et al. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392 (10157), 1553–1598.

Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: a nationally-representative study among US young adults. Computers in Human Behavior, 69 , 1–9.

Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6 (1), 15.

Reece, A. G., Reagan, A. J., Lix, K. L., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific Reports, 7 (1), 13006.

Rideout, V., & Fox, S. (2018). Digital health practices, social media use, and mental well-being among teens and young adults in the U.S. Retrieved from San Francisco, CA: https://www.hopelab.org/reports/pdf/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf . Accessed 10 Jan 2020.

Saha, K., Torous, J., Ernala, S. K., Rizuto, C., Stafford, A., & De Choudhury, M. (2019). A computational study of mental health awareness campaigns on social media. Translational behavioral medicine, 9 (6), 1197–1207.

Schlosser, D. A., Campellone, T., Kim, D., Truong, B., Vergani, S., Ward, C., & Vinogradov, S. (2016). Feasibility of PRIME: a cognitive neuroscience-informed mobile app intervention to enhance motivated behavior and improve quality of life in recent onset schizophrenia. JMIR Research Protocols, 5 (2).

Schlosser, D. A., Campellone, T. R., Truong, B., Etter, K., Vergani, S., Komaiko, K., & Vinogradov, S. (2018). Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia. Schizophrenia Bulletin, 44 (5), 1010–1020.

Schrank, B., Sibitz, I., Unger, A., & Amering, M. (2010). How patients with schizophrenia use the internet: qualitative study. Journal of Medical Internet Research, 12 (5), e70.

Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019). Use of digital mental health for marginalized and underserved populations. Current Treatment Options in Psychiatry, 6 (3), 243–255.

Shatte, A. B., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine, 49 (9), 1426–1448.

Spinzy, Y., Nitzan, U., Becker, G., Bloch, Y., & Fennig, S. (2012). Does the Internet offer social opportunities for individuals with schizophrenia? A cross-sectional pilot study. Psychiatry Research, 198 (2), 319–320.

Stiglic, N., & Viner, R. M. (2019). Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open, 9 (1), e023191.

Sumner, S. A., Galik, S., Mathieu, J., Ward, M., Kiley, T., Bartholow, B., et al. (2019). Temporal and geographic patterns of social media posts about an emerging suicide game. Journal of Adolescent Health, 65 (1), 94–100.

Torous, J., & Keshavan, M. (2016). The role of social media in schizophrenia: evaluating risks, benefits, and potential. Current Opinion in Psychiatry, 29 (3), 190–195.

Torous, J., Chan, S. R., Tan, S. Y.-M., Behrens, J., Mathew, I., Conrad, E. J., et al. (2014a). Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics. JMIR Mental Health, 1 (1), e5.

Torous, J., Friedman, R., & Keshavan, M. (2014b). Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. JMIR mHealth and uHealth, 2 (1), e2.

Torous, J., Wisniewski, H., Bird, B., Carpenter, E., David, G., Elejalde, E., et al. (2019). Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: an interdisciplinary and collaborative approach. Journal of Technology in Behavioral Science, 4 (2), 73–85.

Trefflich, F., Kalckreuth, S., Mergl, R., & Rummel-Kluge, C. (2015). Psychiatric patients' internet use corresponds to the internet use of the general public. Psychiatry Research, 226 , 136–141.

Twenge, J. M., & Campbell, W. K. (2018). Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Preventive Medicine Reports, 12 , 271–283.

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6 (1), 3–17.

Tynes, B. M., Willis, H. A., Stewart, A. M., & Hamilton, M. W. (2019). Race-related traumatic events online and mental health among adolescents of color. Journal of Adolescent Health, 65 (3), 371–377.

Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. Journal of Affective Disorders, 207 , 163–166.

Vayreda, A., & Antaki, C. (2009). Social support and unsolicited advice in a bipolar disorder online forum. Qualitative Health Research, 19 (7), 931–942.

Ventola, C. L. (2014). Social media and health care professionals: benefits, risks, and best practices. Pharmacy and Therapeutics, 39 (7), 491–520.

We Are Social. (2020). Digital in 2020. Retrieved from https://wearesocial.com/global-digital-report-2019 . Accessed 10 Jan 2020.

Webb, H., Jirotka, M., Stahl, B. C., Housley, W., Edwards, A., Williams, M., ... & Burnap, P. (2017). The ethical challenges of publishing Twitter data for research dissemination . Paper presented at the proceedings of the 2017 ACM on Web Science Conference, 339–348.

Williams, M. L., Burnap, P., & Sloan, L. (2017). Towards an ethical framework for publishing twitter data in social research: taking into account users’ views, online context and algorithmic estimation. Sociology, 51 (6), 1149–1168.

Woods, H. C., & Scott, H. (2016). # Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51 , 41–49.

Ybarra, M. L. (2004). Linkages between depressive symptomatology and internet harassment among young regular Internet users. Cyberpsychology & Behavior, 7 (2), 247–257.

Download references

Dr. Naslund is supported by a grant from the National Institute of Mental Health (U19MH113211). Dr. Aschbrenner is supported by a grant from the National Institute of Mental Health (1R01MH110965-01).

Author information

Authors and affiliations.

Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA

John A. Naslund

Digital Mental Health Research Consultant, Mumbai, India

Ameya Bondre

Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

John Torous

Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

Kelly A. Aschbrenner

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to John A. Naslund .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Naslund, J.A., Bondre, A., Torous, J. et al. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J. technol. behav. sci. 5 , 245–257 (2020). https://doi.org/10.1007/s41347-020-00134-x

Download citation

Received : 19 October 2019

Revised : 24 February 2020

Accepted : 17 March 2020

Published : 20 April 2020

Issue Date : September 2020

DOI : https://doi.org/10.1007/s41347-020-00134-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

Social media's growing impact on our lives

Media psychology researchers are beginning to tease apart the ways in which time spent on social media is, and is not, impacting our day-to-day lives.

Social media use has skyrocketed over the past decade and a half. Whereas only five percent of adults in the United States reported using a social media platform in 2005, that number is now around 70 percent .

Growth in the number of people who use Facebook, Instagram, Twitter, and Snapchat and other social media platforms — and the time spent on them—has garnered interest and concern among policymakers, teachers, parents, and clinicians about social media's impacts on our lives and psychological well-being.

While the research is still in its early years — Facebook itself only celebrated its 15 th birthday this year — media psychology researchers are beginning to tease apart the ways in which time spent on these platforms is, and is not, impacting our day-to-day lives.

Social media and relationships

One particularly pernicious concern is whether time spent on social media sites is eating away at face-to-face time, a phenomenon known as social displacement .

Fears about social displacement are longstanding, as old as the telephone and probably older. “This issue of displacement has gone on for more than 100 years,” says Jeffrey Hall, PhD, director of the Relationships and Technology Lab at the University of Kansas. “No matter what the technology is,” says Hall, there is always a “cultural belief that it's replacing face-to-face time with our close friends and family.”

Hall's research interrogates that cultural belief. In one study , participants kept a daily log of time spent doing 19 different activities during weeks when they were and were not asked to abstain from using social media. In the weeks when people abstained from social media, they spent more time browsing the internet, working, cleaning, and doing household chores. However, during these same abstention periods, there was no difference in people's time spent socializing with their strongest social ties.

The upshot? “I tend to believe, given my own work and then reading the work of others, that there's very little evidence that social media directly displaces meaningful interaction with close relational partners,” says Hall. One possible reason for this is because we tend to interact with our close loved ones through several different modalities—such as texts, emails, phone calls, and in-person time.

What about teens?

When it comes to teens, a recent study by Jean Twenge , PhD, professor of psychology at San Diego State University, and colleagues found that, as a cohort, high school seniors heading to college in 2016 spent an “ hour less a day engaging in in-person social interaction” — such as going to parties, movies, or riding in cars together — compared with high school seniors in the late 1980s. As a group, this decline was associated with increased digital media use. However, at the individual level, more social media use was positively associated with more in-person social interaction. The study also found that adolescents who spent the most time on social media and the least time in face-to-face social interactions reported the most loneliness.

While Twenge and colleagues posit that overall face-to-face interactions among teens may be down due to increased time spent on digital media, Hall says there's a possibility that the relationship goes the other way.

Hall cites the work of danah boyd, PhD, principal researcher at Microsoft Research  and the founder of Data & Society . “She [boyd] says that it's not the case that teens are displacing their social face-to-face time through social media. Instead, she argues we got the causality reversed,” says Hall. “We are increasingly restricting teens' ability to spend time with their peers . . . and they're turning to social media to augment it.”

According to Hall, both phenomena could be happening in tandem — restrictive parenting could drive social media use and social media use could reduce the time teens spend together in person — but focusing on the latter places the culpability more on teens while ignoring the societal forces that are also at play.

The evidence is clear about one thing: Social media is popular among teens. A 2018 Common Sense Media report found that 81 percent of teens use social media, and more than a third report using social media sites multiple times an hour. These statistics have risen dramatically over the past six years, likely driven by increased access to mobile devices. Rising along with these stats is a growing interest in the impact that social media is having on teen cognitive development and psychological well-being.

“What we have found, in general, is that social media presents both risks and opportunities for adolescents,” says Kaveri Subrahmanyam, PhD, a developmental psychologist, professor at Cal State LA, and associate director of the Children's Digital Media Center, Los Angeles .

Risks of expanding social networks

Social media benefits teens by expanding their social networks and keeping them in touch with their peers and far-away friends and family. It is also a creativity outlet. In the Common Sense Media report, more than a quarter of teens said that “social media is ‘extremely' or ‘very' important for them for expressing themselves creatively.”

But there are also risks. The Common Sense Media survey found that 13 percent of teens reported being cyberbullied at least once. And social media can be a conduit for accessing inappropriate content like violent images or pornography. Nearly two-thirds of teens who use social media said they “'often' or ‘sometimes' come across racist, sexist, homophobic, or religious-based hate content in social media.”

With all of these benefits and risks, how is social media affecting cognitive development? “What we have found at the Children's Digital Media Center is that a lot of digital communication use and, in particular, social media use seems to be connected to offline developmental concerns,” says Subrahmanyam. “If you look at the adolescent developmental literature, the core issues facing youth are sexuality, identity, and intimacy,” says Subrahmanyam.

Her research suggests that different types of digital communication may involve different developmental issues. For example, she has found that teens frequently talked about sex in chat rooms , whereas their use of blogs and social media appears to be more concerned with self-presentation and identity construction.

In particular, exploring one's identity appears to be a crucial use of visually focused social media sites for adolescents. “Whether it's Facebook, whether it's Instagram, there's a lot of strategic self presentation, and it does seem to be in the service of identity,” says Subrahmanyam. “I think where it gets gray is that we don't know if this is necessarily beneficial or if it harms.”

Remaining questions

“It's important to develop a coherent identity,” she says. “But within the context of social media — when it's not clear that people are necessarily engaging in real self presentation and there's a lot of ideal-self or false-self presentation — is that good?”

There are also more questions than answers when it comes to how social media affects the development of intimate relationships during adolescence. Does having a wide network of contacts — as is common in social media—lead to more superficial interactions and hinder intimacy? Or, perhaps more important, “Is the support that you get online as effective as the support that you get offline?” ponders Subrahmanyam. “We don't know that necessarily.”

Based on her own research comparing text messages and face-to-face interactions, she says: “My hypothesis is that maybe digital interactions may be a little more ephemeral, they're a little more fleeting, and you feel good, but that the feeling is lost quickly versus face-to-face interaction.”

However, she notes that today's teens — being tech natives — may get less hung up on the online/offline dichotomy. “ We tend to think about online and offline as disconnected, but we have to recognize that for youth . . . there's so much more fluidity and connectedness between the real and the physical and the offline and the online,” she says.

In fact, growing up with digital technology may be changing teen brain development in ways we don't yet know — and these changes may, in turn, change how teens relate to technology. “Because the exposure to technology is happening so early, we have to be mindful of the possibility that perhaps there are changes happening at a neural level with early exposure,” says Subrahmanyam. “How youths interact with technology could just be qualitatively different from how we do it.”

In part two of this article , we will look at how social media affects psychological well-being and ways of using social media that are likely to amplify its benefits and decrease its harms.

Related and recent

Recommended reading.

APA Member Benefits

Connecting with peers

APA Community

Automate HIPAA Compliance

New products available

APA Merch Store

Competitive low rates

Student Loan Refinancing

Contact APA Membership

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

• Social Media Use in 2021

A majority of Americans say they use YouTube and Facebook, while use of Instagram, Snapchat and TikTok is especially common among adults under 30.

Table of contents.

• Acknowledgments
• Methodology

To better understand Americans’ use of social media, online platforms and messaging apps, Pew Research Center surveyed 1,502 U.S. adults from Jan. 25 to Feb. 8, 2021, by cellphone and landline phone. The survey was conducted by interviewers under the direction of Abt Associates and is weighted to be representative of the U.S. adult population by gender, race, ethnicity, education and other categories. Here are the  questions used for this report , along with responses, and  its methodology .

Despite a string of controversies and the public’s relatively negative sentiments about aspects of social media, roughly seven-in-ten Americans say they ever use any kind of social media site – a share that has remained relatively stable over the past five years, according to a new Pew Research Center survey of U.S. adults.

Beyond the general question of overall social media use, the survey also covers use of individual sites and apps. YouTube and Facebook continue to dominate the online landscape, with 81% and 69%, respectively, reporting ever using these sites. And YouTube and Reddit were the only two platforms measured that saw statistically significant growth since 2019 , when the Center last polled on this topic via a phone survey.

When it comes to the other platforms in the survey, 40% of adults say they ever use Instagram and about three-in-ten report using Pinterest or LinkedIn. One-quarter say they use Snapchat, and similar shares report being users of Twitter or WhatsApp. TikTok – an app for sharing short videos – is used by 21% of Americans, while 13% say they use the neighborhood-focused platform Nextdoor.

Even as other platforms do not nearly match the overall reach of YouTube or Facebook, there are certain sites or apps, most notably Instagram, Snapchat and TikTok, that have an especially strong following among young adults. In fact, a majority of 18- to 29-year-olds say they use Instagram (71%) or Snapchat (65%), while roughly half say the same for TikTok.

These findings come from a nationally representative survey of 1,502 U.S. adults conducted via telephone Jan. 25-Feb.8, 2021.

With the exception of YouTube and Reddit, most platforms show little growth since 2019

YouTube is the most commonly used online platform asked about in this survey, and there’s evidence that its reach is growing. Fully 81% of Americans say they ever use the video-sharing site, up from 73% in 2019. Reddit was the only other platform polled about that experienced statistically significant growth during this time period – increasing from 11% in 2019 to 18% today. 

Facebook’s growth has leveled off over the last five years, but it remains one of the most widely used social media sites among adults in the United States: 69% of adults today say they ever use the site, equaling the share who said this two years prior.  

Similarly, the respective shares of Americans who report using Instagram, Pinterest, LinkedIn, Snapchat, Twitter and WhatsApp are statistically unchanged since 2019 . This represents a broader trend that extends beyond the past two years in which the rapid adoption of most of these sites and apps seen in the last decade has slowed. (This was the first year the Center asked about TikTok via a phone poll and the first time it has surveyed about Nextdoor.)

Adults under 30 stand out for their use of Instagram, Snapchat and TikTok

When asked about their social media use more broadly – rather than their use of specific platforms – 72% of Americans say they ever use social media sites.

In a pattern consistent with past Center studies on social media use, there are some stark age differences. Some 84% of adults ages 18 to 29 say they ever use any social media sites, which is similar to the share of those ages 30 to 49 who say this (81%). By comparison, a somewhat smaller share of those ages 50 to 64 (73%) say they use social media sites, while fewer than half of those 65 and older (45%) report doing this.

These age differences generally extend to use of specific platforms, with younger Americans being more likely than their older counterparts to use these sites – though the gaps between younger and older Americans vary across platforms.

Majorities of 18- to 29-year-olds say they use Instagram or Snapchat and about half say they use TikTok, with those on the younger end of this cohort – ages 18 to 24 – being especially likely to report using Instagram (76%), Snapchat (75%) or TikTok (55%). 1 These shares stand in stark contrast to those in older age groups. For instance, while 65% of adults ages 18 to 29 say they use Snapchat, just 2% of those 65 and older report using the app – a difference of 63 percentage points.

Additionally, a vast majority of adults under the age of 65 say they use YouTube. Fully 95% of those 18 to 29 say they use the platform, along with 91% of those 30 to 49 and 83% of adults 50 to 64. However, this share drops substantially – to 49% – among those 65 and older.

By comparison, age gaps between the youngest and oldest Americans are narrower for Facebook. Fully 70% of those ages 18 to 29 say they use the platform, and those shares are statistically the same for those ages 30 to 49 (77%) or ages 50 to 64 (73%). Half of those 65 and older say they use the site – making Facebook and YouTube the two most used platforms among this older population.

Other sites and apps stand out for their demographic differences:

• Instagram: About half of Hispanic (52%) and Black Americans (49%) say they use the platform, compared with smaller shares of White Americans (35%) who say the same. 2
• WhatsApp: Hispanic Americans (46%) are far more likely to say they use WhatsApp than Black (23%) or White Americans (16%). Hispanics also stood out for their WhatsApp use in the Center’s previous surveys on this topic.
• LinkedIn: Those with higher levels of education are again more likely than those with lower levels of educational attainment to report being LinkedIn users. Roughly half of adults who have a bachelor’s or advanced degree (51%) say they use LinkedIn, compared with smaller shares of those with some college experience (28%) and those with a high school diploma or less (10%).
• Pinterest: Women continue to be far more likely than men to say they use Pinterest when compared with male counterparts, by a difference of 30 points (46% vs. 16%).
• Nextdoor: There are large differences in use of this platform by community type. Adults living in urban (17%) or suburban (14%) areas are more likely to say they use Nextdoor. Just 2% of rural Americans report using the site.

A majority of Facebook, Snapchat and Instagram users say they visit these platforms on a daily basis

While there has been much written about Americans’ changing relationship with Facebook , its users remain quite active on the platform. Seven-in-ten Facebook users say they use the site daily, including 49% who say they use the site several times a day. (These figures are statistically unchanged from those reported in the Center’s 2019 survey about social media use.)  

Smaller shares – though still a majority – of Snapchat or Instagram users report visiting these respective platforms daily (59% for both). And being active on these sites is especially common for younger users. For instance, 71% of Snapchat users ages 18 to 29 say they use the app daily, including six-in-ten who say they do this multiple times a day. The pattern is similar for Instagram: 73% of 18- to 29-year-old Instagram users say they visit the site every day, with roughly half (53%) reporting they do so several times per day.

YouTube is used daily by 54% if its users, with 36% saying they visit the site several times a day. By comparison, Twitter is used less frequently, with fewer than half of its users (46%) saying they visit the site daily.

• Due to a limited sample size, figures for those ages 25 to 29 cannot be reported on separately. ↩
• There were not enough Asian American respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout this report. ↩

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

• Social Media
• User Demographics

Many Israelis say social media content about the Israel-Hamas war should be censored

Whatsapp and facebook dominate the social media landscape in middle-income nations, germans stand out for their comparatively light use of social media, majorities in most countries surveyed say social media is good for democracy.

• Social Media Fact Sheet

Most Popular

Report materials.

• 2021 Core Trends Survey

901 E St. NW, Suite 300 Washington, DC 20004 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

Best Social Media Research Topics | Inspiration & Ideas

Introduction

What distinguishes social media from other communication, why research social media, what can i research about social media, conducting research on social media.

Social media has become an integral part of modern communication, influencing how people connect, share information, and interact with the world. As a rapidly evolving field, it presents a wealth of opportunities for research that can offer valuable insights into societal trends, behavioral patterns, and technological advancements. This article aims to provide inspiration and ideas for selecting compelling social media research topics. We’ll explore what makes social media unique, the importance of studying it, and offer suggestions for areas you can investigate.

Social media is a unique form of communication that differs significantly from traditional methods such as face-to-face interactions, phone calls, or even emails. Several key characteristics set social media apart, making it a fascinating area for research.

Interactivity and user-generated content

One of the most distinctive features of social media is its interactivity. Unlike traditional media, where communication is typically one-way, social media platforms enable two-way interactions between users. This interactivity allows users to not only consume content but also to create and share their own, leading to an environment rich in user-generated content. This aspect of social media fosters a participatory culture where individuals can contribute to discussions, share their perspectives, and engage with others in real-time.

Networked communication

Social media operates on a networked model of communication, where information is shared across a web of interconnected users. This networked nature allows content to spread rapidly through shares, likes, comments, and other forms of engagement. The viral potential of social media content is a key characteristic that distinguishes it from other forms of communication, where information dissemination is often more controlled and linear.

Personalization and algorithms

Another defining feature of social media is the use of algorithms to personalize user experiences. These algorithms analyze user behavior, preferences, and interactions to curate content that is most relevant to each individual. This level of personalization is unmatched by traditional communication methods and has profound implications for how people receive information, form opinions, and engage with the world around them. The algorithm-driven nature of social media also raises important questions about echo chambers, filter bubbles, and the impact of personalized content on societal discourse.

Multimedia integration

Social media platforms seamlessly integrate various forms of multimedia, including text, images, videos , and live streams. This multimedia approach enhances the richness of social media communication and allows users to express themselves in diverse and creative ways. The ability to combine different media types in a single platform sets social media apart from other communication methods, which may be more limited in their use of media.

Global reach and immediacy

Finally, a solid social media presence offers unprecedented global reach and immediacy. With social media exposure, users can connect with others across the world instantly, breaking down geographical barriers and enabling cross-cultural communication. The real-time nature of social media allows for immediate responses and updates, making it a powerful tool for sharing news, organizing events, and mobilizing movements for marketing endeavors, political campaigns, and other collective efforts.

Researching social media is crucial because of its pervasive influence on various aspects of society, including communication, culture, politics, and even mental health. As social media continues to evolve and integrate into everyday life, understanding its impact becomes increasingly important for several reasons.

First, social media shapes public opinion and discourse in ways that traditional media cannot. The speed at which information spreads on platforms like Twitter/X, Facebook, and Instagram can amplify voices and ideas, often creating significant cultural or political movements. Studying these phenomena can reveal insights into how public opinion is formed, how misinformation spreads, and how social movements gain traction.

Second, social media platforms collect vast amounts of data about user behavior, preferences, and interactions. This data offers a unique opportunity for researchers to analyze trends, understand user engagement, and explore the effects of algorithmic content curation. By examining these aspects, researchers can shed light on how social media influences decision-making, consumer behavior, and even voting patterns.

Moreover, social media has a profound impact on mental health and well-being. The constant connectivity and exposure to curated lives can lead to issues such as anxiety, depression, and feelings of inadequacy. Research in this area can help identify the factors contributing to these mental health challenges and guide the development of interventions or policies to mitigate them.

Finally, as social media becomes a key tool for marketing, education, and even governance, understanding its mechanisms and effects is vital for professionals across various fields. Whether it’s to improve social media marketing strategies, enhance educational outreach, or design more effective public policies, social media research papers provide valuable insights that can inform practice and policy.

Make the most of your data with ATLAS.ti

For the most powerful data analysis, turn to ATLAS.ti. Get started with a free trial.

Choosing a social media research topic can be a difficult decision among numerous research opportunities across various disciplines. Here are three key areas to consider when selecting a research topic related to social media: societal impact, psychological effects, and technological advancements.

Societal impact of social media

One of the most significant aspects of social media is its profound impact on society. Researching this area can provide valuable insights into how social media influences cultural norms, political movements, and social behavior.

• Social media and political movements : Social media platforms have played a crucial role in organizing and mobilizing political movements around the world. From the Arab Spring to the Black Lives Matter movement, these platforms have facilitated the rapid spread of information and coordination among activists. Researching the role of social media in political movements can reveal how these platforms influence public opinion, empower grassroots movements, and even shape election outcomes. Additionally, you can explore the potential downsides, such as the spread of misinformation or the role of bots and fake accounts in manipulating political discourse.
• Cultural diversity and social media : Social media platforms are global in reach, connecting people from different cultures and backgrounds. This connectivity can promote cultural diversity by exposing users to new ideas, traditions, and perspectives. However, it can also lead to cultural homogenization, where dominant cultures overshadow minority voices. Researching the impact of social media on cultural diversity can explore how these platforms either promote or hinder cultural exchange and the preservation of cultural identities. You might also investigate the role of social media in fostering cross-cultural understanding or exacerbating cultural tensions.
• Social media and public health campaigns : Social media has become a vital tool for public health communication, particularly during global crises like the COVID-19 pandemic. Platforms like Twitter/X and Facebook have been used to disseminate important health information, raise awareness about preventive measures, and combat misinformation. Researching the effectiveness of social media in public health campaigns can provide insights into how these platforms can be used to promote healthy behaviors, increase vaccination rates, and improve public health outcomes. Additionally, you can examine the challenges of combating health misinformation and the social media strategies that have been successful in addressing it.

Psychological effects of social media

In studying social media, psychology has many potential theoretical and practical research questions . Understanding how these platforms influence mental health, self-esteem, and social interactions is crucial for developing strategies to mitigate negative impacts and enhance positive outcomes.

• Social media and mental health : One of the most extensively studied areas is the relationship between social media use and mental health. Research has shown that excessive use of social media can lead to negative outcomes such as anxiety, depression, and loneliness. However, these effects can vary depending on factors like age, personality, and the type of content consumed. Researching the impact of social media on mental health can involve exploring the specific mechanisms through which social media affects well-being, such as comparison with others, cyberbullying, or the pressure to present a perfect image online. You might also investigate the potential benefits of social media, such as providing support networks for individuals with mental health challenges.
• The role of social media in shaping self-identity : Social media platforms provide users with the tools to curate and present their identities online. This process of identity construction can have both positive and negative effects. On the one hand, social media can empower individuals to express themselves and connect with like-minded communities. On the other hand, the pressure to conform to societal standards and the constant exposure to idealized images can lead to issues like low self-esteem and body image concerns. Researching the role of social media in shaping self-identity can involve examining how different groups (e.g., teenagers, adults, marginalized communities) use social media to explore and express their identities. Additionally, you can study the impact of social media on self-perception and the ways in which online interactions influence offline behaviors and attitudes.
• Social media addiction : As social media becomes increasingly integrated into daily life, the phenomenon of social media addiction has garnered significant attention. Social media addiction is characterized by excessive use of social media platforms, leading to negative consequences in an individual's personal, academic, or professional life. Researching social media addiction can involve exploring the factors that contribute to addictive behaviors, such as the design of social media platforms, individual personality traits, and social influences. Additionally, you can investigate the impact of social media addiction on mental health, relationships, and productivity, as well as potential interventions to address this issue.

Technological advancements and social media

Technological advancements play a pivotal role in shaping the evolution of social media platforms. Understanding these advancements and their implications can offer valuable insights into the future of social media and its impact on society.

• Artificial intelligence and social media algorithms : Artificial intelligence (AI) is increasingly being used to power the algorithms that drive content curation on social media platforms. These algorithms analyze user behavior, preferences, and interactions to deliver personalized content, ads, and recommendations. While AI can enhance user experience by providing relevant content, it also raises concerns about privacy, echo chambers, and the manipulation of information. Researching the role of AI in social media can involve exploring how these algorithms work, their impact on user behavior, and the ethical implications of AI-driven content curation. Additionally, you can study the potential of AI to combat issues like misinformation, hate speech, and online harassment.
• The evolution of social media platforms : Social media platforms are constantly evolving, with new features, tools, and platforms emerging regularly. Understanding the technological trends driving these changes can provide insights into the future of social media. Researching the evolution of social media platforms can involve examining how new technologies, such as augmented reality (AR), virtual reality (VR), and live streaming, are being integrated into social media. You can also explore the impact of these technologies on user engagement, content creation, and social interactions. Additionally, consider investigating the rise of niche social media platforms and how they cater to specific communities or interests.
• Data privacy and security on social media : As social media platforms collect vast amounts of user data, concerns about data privacy and security have become increasingly prominent. Researching data privacy on social media can involve exploring the ways in which platforms collect, store, and use user data, as well as the potential risks associated with data breaches and unauthorized access. Additionally, you can examine the impact of data privacy regulations, such as the General Data Protection Regulation (GDPR), on social media platforms and their practices. Studying user perceptions of data privacy and their behaviors in response to privacy concerns can also provide valuable insights into how social media platforms can build trust with their users.

Conducting research on social media requires careful consideration of the methodologies employed, the ethical implications involved, and the approaches to data analysis. Each of these factors plays a crucial role in ensuring that the research is both rigorous and responsible.

Choosing appropriate methodologies

Selecting the appropriate research methodology is a foundational step in addressing social media research questions . The choice of methodology largely depends on the research questions and objectives. Qualitative methods, such as in-depth interviews , focus groups , and content analysis , offer valuable insights into the subjective experiences and perceptions of social media users.

For example, interviews can reveal how individuals construct and present their identities online, while content analysis allows researchers to explore patterns and themes within social media interactions, such as how users discuss specific topics or respond to particular events.

On the other hand, quantitative methods, like surveys and experiments, are essential for gathering data that can be measured and analyzed statistically. Surveys can provide a broad overview of user behaviors and attitudes across large populations, enabling researchers to identify trends and correlations. Experiments, meanwhile, are useful for testing specific hypotheses, such as the impact of social media use on academic performance or mental health.

In some cases, combining qualitative and quantitative methods in a mixed-methods approach can offer a more comprehensive understanding of the phenomena being studied, allowing researchers to explore both the depth and breadth of social media interactions.

Accounting for ethical issues

Ethical considerations are paramount in social media research, given the sensitive nature of the data often involved. One of the primary ethical challenges is obtaining informed consent from participants , especially in environments where users may not be fully aware that their posts or interactions are being analyzed.

Researchers must navigate this challenge by ensuring that their methods of obtaining consent are clear and transparent, particularly when dealing with content that users might consider private, despite being posted on public platforms.

Protecting the privacy and confidentiality of participants is another critical concern. This involves anonymizing data to prevent the identification of individuals and securing the data to protect it from unauthorized access. Researchers must also be sensitive to the potential risks associated with their studies, particularly when dealing with vulnerable populations or sensitive topics such as mental health or political beliefs.

Transparency in the research process is essential; researchers should openly communicate their intentions, methods, and any potential conflicts of interest, ensuring that participants understand how their data will be used. Engaging with the communities involved in the research can also help to mitigate ethical concerns, as it fosters trust and collaboration, making the research process more inclusive and respectful of participants' rights and perspectives.

Conducting data analysis

The analysis of social media data presents its own set of challenges, given the vast amount of information that can be generated on these platforms. Effective data analysis requires not only technical proficiency but also a deep understanding of the social context in which the data is produced.

Data cleaning and preparation are crucial initial steps, as social media data often contains noise and irrelevant information that can skew results. Researchers must carefully filter and organize their data to ensure that the analysis is accurate and meaningful. Once the data is prepared, researchers can apply various analytical techniques, depending on the research objectives.

For qualitative data , thematic analysis can be used to identify recurring themes and patterns within the content, providing insights into user behaviors and perceptions. Quantitative data , on the other hand, may require statistical analysis to uncover correlations, trends, or causal relationships.

Throughout the analysis process, it is important for researchers to remain mindful of the limitations of their data and the potential biases that may influence their findings. This includes being aware of the algorithms that social media platforms use to curate content, which can impact the data collected and the conclusions drawn from it.

Collect, organize, and analyze data with ATLAS.ti

Turn qualitative data into insights with our intuitive and powerful platform. Download a free trial today.

Queensland research finds young people 'burnt out and in need of help'

By Claudia Williams

Topic: Mental Health

New research shows almost nine out of 10 young Queenslanders have seen a negative change in their health and wellbeing in the past year.  ( ABC News: Stephanie Anderson )

It is impossible to ignore the negative impacts of smartphones and social media on the mental health and wellbeing of young people, Queensland’s chief health officer says. 

The comments come as new research shows almost nine out of 10 young Queenslanders have seen a negative change in their health and wellbeing in the past year. 

The survey of 1,424 young people conducted by the state's prevention agency, Health and Wellbeing Queensland, found more than half of respondents reported feeling stressed or anxious.

Chief Health Officer Dr John Gerrard said while less people were dying from heart disease and strokes, the mental health of young people was "getting worse very rapidly".

"It appears to be a real phenomenon and not the result of better reporting," he said. "I believe this is a very significant concern.

"One of the most dramatic indicators is the instances of hospitalisation due to self-harm in young children aged 10 to 14 has almost [tripled] over the last decade."

John Gerrard says the mental ill-health of young people is a very real phenomenon being seen across the world. ( ABC News: Claudia Williams )

Dr Gerrard said the mental health decline in young people had been seen on a global scale since 2010, in the years following the release of the first smartphone.

He said there were no simple solutions, adding the community at-large has not spoken about "this enough".

"It is not clear at this stage what to do about this specific problem, but I have been meeting with Commonwealth agencies to discuss these issues."

'Burnt out and in need of help'

The research, commissioned by the Queensland government, found more than half of those aged 15 to 24 reported feeling tired for no reason or that everything was an effort in the four weeks prior to being surveyed.

Health and Wellbeing Queensland deputy chief executive Gemma Hodgetts said these were the warning signs of a generation "burnt out and in need of help". 

"Young Queenslanders who should be our most vibrant, energetic and hopeful generation are struggling," she said. 

Gemma Hodgetts says the research shows young people are struggling. ( ABC News: Claudia Williams )

"Almost one in two Queenslanders will experience mental ill-health in their lifetime ... about 75 per cent of mental disorders emerge before the age of 24 years, so we need to act now."

The research found those experiencing mental health challenges were more likely to rate their health significantly lower.

The report said the findings suggest increased stress, along with poorer diets, may be negatively impacting the mental health of young Queenslanders, particularly young adults.

According to the research, women, girls and mothers are also more likely to experience negative impacts, which may in part be due to their lower activity levels.

Ms Hodgetts said the report laid the foundation for an Australian-first strategy which would take a deliberate wellbeing approach to mental health.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

Similar content being viewed by others

Research progress and intellectual structure of design for digital equity (DDE): A bibliometric analysis based on citespace

Exploring the role of interaction in older-adult service innovation: insights from the testing stage

Smart device interest, perceived usefulness, and preferences in rural Alabama seniors

Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

Abdi S, de Witte L, Hawley M (2020) Emerging technologies with potential care and support applications for older people: review of gray literature. JMIR Aging 3(2):e17286. https://doi.org/10.2196/17286

Article   PubMed   PubMed Central   Google Scholar  

Achuthan K, Nair VK, Kowalski R, Ramanathan S, Raman R (2023) Cyberbullying research—Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Comput Human Behav 140:107566. https://doi.org/10.1016/j.chb.2022.107566

Article   Google Scholar  

Ahmad A, Mozelius P (2022) Human-Computer Interaction for Older Adults: a Literature Review on Technology Acceptance of eHealth Systems. J Eng Res Sci 1(4):119–126. https://doi.org/10.55708/js0104014

Ale Ebrahim N, Salehi H, Embi MA, Habibi F, Gholizadeh H, Motahar SM (2014) Visibility and citation impact. Int Educ Stud 7(4):120–125. https://doi.org/10.5539/ies.v7n4p120

Amin MS, Johnson VL, Prybutok V, Koh CE (2024) An investigation into factors affecting the willingness to disclose personal health information when using AI-enabled caregiver robots. Ind Manag Data Syst 124(4):1677–1699. https://doi.org/10.1108/IMDS-09-2023-0608

Baer NR, Vietzke J, Schenk L (2022) Middle-aged and older adults’ acceptance of mobile nutrition and fitness apps: a systematic mixed studies review. PLoS One 17(12):e0278879. https://doi.org/10.1371/journal.pone.0278879

Barnard Y, Bradley MD, Hodgson F, Lloyd AD (2013) Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Comput Human Behav 29(4):1715–1724. https://doi.org/10.1016/j.chb.2013.02.006

Berkowsky RW, Sharit J, Czaja SJ (2017) Factors predicting decisions about technology adoption among older adults. Innov Aging 3(1):igy002. https://doi.org/10.1093/geroni/igy002

Braun MT (2013) Obstacles to social networking website use among older adults. Comput Human Behav 29(3):673–680. https://doi.org/10.1016/j.chb.2012.12.004

Article   MathSciNet   Google Scholar  

Campo-Prieto P, Rodríguez-Fuentes G, Cancela-Carral JM (2021) Immersive virtual reality exergame promotes the practice of physical activity in older people: An opportunity during COVID-19. Multimodal Technol Interact 5(9):52. https://doi.org/10.3390/mti5090052

Chen C (2006) CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317

Chen C, Dubin R, Kim MC (2014) Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther 14(9):1295–1317. https://doi.org/10.1517/14712598.2014.920813

Article   PubMed   Google Scholar  

Chen C, Leydesdorff L (2014) Patterns of connections and movements in dual‐map overlays: A new method of publication portfolio analysis. J Assoc Inf Sci Technol 65(2):334–351. https://doi.org/10.1002/asi.22968

Chen J, Wang C, Tang Y (2022) Knowledge mapping of volunteer motivation: A bibliometric analysis and cross-cultural comparative study. Front Psychol 13:883150. https://doi.org/10.3389/fpsyg.2022.883150

Chen JY, Liu YD, Dai J, Wang CL (2023) Development and status of moral education research: Visual analysis based on knowledge graph. Front Psychol 13:1079955. https://doi.org/10.3389/fpsyg.2022.1079955

Chen K, Chan AH (2011) A review of technology acceptance by older adults. Gerontechnology 10(1):1–12. https://doi.org/10.4017/gt.2011.10.01.006.00

Chen K, Chan AH (2014) Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5):635–652. https://doi.org/10.1080/00140139.2014.895855

Chen K, Zhang Y, Fu X (2019) International research collaboration: An emerging domain of innovation studies? Res Policy 48(1):149–168. https://doi.org/10.1016/j.respol.2018.08.005

Chen X, Hu Z, Wang C (2024) Empowering education development through AIGC: A systematic literature review. Educ Inf Technol 1–53. https://doi.org/10.1007/s10639-024-12549-7

Chen Y, Chen CM, Liu ZY, Hu ZG, Wang XW (2015) The methodology function of CiteSpace mapping knowledge domains. Stud Sci Sci 33(2):242–253. https://doi.org/10.16192/j.cnki.1003-2053.2015.02.009

Codfrey GS, Baharum A, Zain NHM, Omar M, Deris FD (2022) User Experience in Product Design and Development: Perspectives and Strategies. Math Stat Eng Appl 71(2):257–262. https://doi.org/10.17762/msea.v71i2.83

Dai J, Zhang X, Wang CL (2024) A meta-analysis of learners’ continuance intention toward online education platforms. Educ Inf Technol 1–36. https://doi.org/10.1007/s10639-024-12654-7

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008

Delmastro F, Dolciotti C, Palumbo F, Magrini M, Di Martino F, La Rosa D, Barcaro U (2018) Long-term care: how to improve the quality of life with mobile and e-health services. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 12–19. IEEE. https://doi.org/10.1109/WiMOB.2018.8589157

Dupuis K, Tsotsos LE (2018) Technology for remote health monitoring in an older population: a role for mobile devices. Multimodal Technol Interact 2(3):43. https://doi.org/10.3390/mti2030043

Ferguson C, Hickman LD, Turkmani S, Breen P, Gargiulo G, Inglis SC (2021) Wearables only work on patients that wear them”: Barriers and facilitators to the adoption of wearable cardiac monitoring technologies. Cardiovasc Digit Health J 2(2):137–147. https://doi.org/10.1016/j.cvdhj.2021.02.001

Fisk AD, Czaja SJ, Rogers WA, Charness N, Sharit J (2020) Designing for older adults: Principles and creative human factors approaches. CRC Press. https://doi.org/10.1201/9781420080681

Friesen S, Brémault-Phillips S, Rudrum L, Rogers LG (2016) Environmental design that supports healthy aging: Evaluating a new supportive living facility. J Hous Elderly 30(1):18–34. https://doi.org/10.1080/02763893.2015.1129380

Garcia Reyes EP, Kelly R, Buchanan G, Waycott J (2023) Understanding Older Adults’ Experiences With Technologies for Health Self-management: Interview Study. JMIR Aging 6:e43197. https://doi.org/10.2196/43197

Geng Z, Wang J, Liu J, Miao J (2024) Bibliometric analysis of the development, current status, and trends in adult degenerative scoliosis research: A systematic review from 1998 to 2023. J Pain Res 17:153–169. https://doi.org/10.2147/JPR.S437575

González A, Ramírez MP, Viadel V (2012) Attitudes of the elderly toward information and communications technologies. Educ Gerontol 38(9):585–594. https://doi.org/10.1080/03601277.2011.595314

Guner H, Acarturk C (2020) The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Univ Access Inf Soc 19(2):311–330. https://doi.org/10.1007/s10209-018-0642-4

Halim I, Saptari A, Perumal PA, Abdullah Z, Abdullah S, Muhammad MN (2022) A Review on Usability and User Experience of Assistive Social Robots for Older Persons. Int J Integr Eng 14(6):102–124. https://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/8566

He Y, He Q, Liu Q (2022) Technology acceptance in socially assistive robots: Scoping review of models, measurement, and influencing factors. J Healthc Eng 2022(1):6334732. https://doi.org/10.1155/2022/6334732

Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot 2:361–375. https://doi.org/10.1007/s12369-010-0068-5

Ho A (2020) Are we ready for artificial intelligence health monitoring in elder care? BMC Geriatr 20(1):358. https://doi.org/10.1186/s12877-020-01764-9

Hoque R, Sorwar G (2017) Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform 101:75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002

Hota PK, Subramanian B, Narayanamurthy G (2020) Mapping the intellectual structure of social entrepreneurship research: A citation/co-citation analysis. J Bus Ethics 166(1):89–114. https://doi.org/10.1007/s10551-019-04129-4

Huang R, Yan P, Yang X (2021) Knowledge map visualization of technology hotspots and development trends in China’s textile manufacturing industry. IET Collab Intell Manuf 3(3):243–251. https://doi.org/10.1049/cim2.12024

Article   ADS   Google Scholar  

Jing Y, Wang C, Chen Y, Wang H, Yu T, Shadiev R (2023) Bibliometric mapping techniques in educational technology research: A systematic literature review. Educ Inf Technol 1–29. https://doi.org/10.1007/s10639-023-12178-6

Jing YH, Wang CL, Chen ZY, Shen SS, Shadiev R (2024a) A Bibliometric Analysis of Studies on Technology-Supported Learning Environments: Hotopics and Frontier Evolution. J Comput Assist Learn 1–16. https://doi.org/10.1111/jcal.12934

Jing YH, Wang HM, Chen XJ, Wang CL (2024b) What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanit Soc Sci Commun 11:319. https://doi.org/10.1057/s41599-024-02751-w

Kamrani P, Dorsch I, Stock WG (2021) Do researchers know what the h-index is? And how do they estimate its importance? Scientometrics 126(7):5489–5508. https://doi.org/10.1007/s11192-021-03968-1

Kim HS, Lee KH, Kim H, Kim JH (2014) Using mobile phones in healthcare management for the elderly. Maturitas 79(4):381–388. https://doi.org/10.1016/j.maturitas.2014.08.013

Article   MathSciNet   PubMed   Google Scholar  

Kleinberg J (2002) Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 91–101. https://doi.org/10.1145/775047.775061

Kruse C, Fohn J, Wilson N, Patlan EN, Zipp S, Mileski M (2020) Utilization barriers and medical outcomes commensurate with the use of telehealth among older adults: systematic review. JMIR Med Inform 8(8):e20359. https://doi.org/10.2196/20359

Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40. https://doi.org/10.1007/s10660-021-09464-1

Kwiek M (2021) What large-scale publication and citation data tell us about international research collaboration in Europe: Changing national patterns in global contexts. Stud High Educ 46(12):2629–2649. https://doi.org/10.1080/03075079.2020.1749254

Lee C, Coughlin JF (2015) PERSPECTIVE: Older adults’ adoption of technology: an integrated approach to identifying determinants and barriers. J Prod Innov Manag 32(5):747–759. https://doi.org/10.1111/jpim.12176

Lee CH, Wang C, Fan X, Li F, Chen CH (2023) Artificial intelligence-enabled digital transformation in elderly healthcare field: scoping review. Adv Eng Inform 55:101874. https://doi.org/10.1016/j.aei.2023.101874

Leydesdorff L, Rafols I (2012) Interactive overlays: A new method for generating global journal maps from Web-of-Science data. J Informetr 6(2):318–332. https://doi.org/10.1016/j.joi.2011.11.003

Li J, Ma Q, Chan AH, Man S (2019) Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 75:162–169. https://doi.org/10.1016/j.apergo.2018.10.006

Article   ADS   PubMed   Google Scholar  

Li X, Zhou D (2020) Product design requirement information visualization approach for intelligent manufacturing services. China Mech Eng 31(07):871, http://www.cmemo.org.cn/EN/Y2020/V31/I07/871

Google Scholar  

Lin Y, Yu Z (2024a) An integrated bibliometric analysis and systematic review modelling students’ technostress in higher education. Behav Inf Technol 1–25. https://doi.org/10.1080/0144929X.2024.2332458

Lin Y, Yu Z (2024b) A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interact Technol Smart Educ 21(2):189–213. https://doi.org/10.1108/ITSE-12-2022-0165

Liu L, Duffy VG (2023) Exploring the future development of Artificial Intelligence (AI) applications in chatbots: a bibliometric analysis. Int J Soc Robot 15(5):703–716. https://doi.org/10.1007/s12369-022-00956-0

Liu R, Li X, Chu J (2022) Evolution of applied variables in the research on technology acceptance of the elderly. In: International Conference on Human-Computer Interaction, Cham: Springer International Publishing, pp 500–520. https://doi.org/10.1007/978-3-031-05581-23_5

Luijkx K, Peek S, Wouters E (2015) “Grandma, you should do it—It’s cool” Older Adults and the Role of Family Members in Their Acceptance of Technology. Int J Environ Res Public Health 12(12):15470–15485. https://doi.org/10.3390/ijerph121214999

Lussier M, Lavoie M, Giroux S, Consel C, Guay M, Macoir J, Bier N (2018) Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: a systematic review. IEEE J Biomed Health Inform 23(2):838–847. https://doi.org/10.1109/JBHI.2018.2834317

López-Robles JR, Otegi-Olaso JR, Porto Gomez I, Gamboa-Rosales NK, Gamboa-Rosales H, Robles-Berumen H (2018) Bibliometric network analysis to identify the intellectual structure and evolution of the big data research field. In: International Conference on Intelligent Data Engineering and Automated Learning, Cham: Springer International Publishing, pp 113–120. https://doi.org/10.1007/978-3-030-03496-2_13

Ma Q, Chan AH, Chen K (2016) Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl Ergon 54:62–71. https://doi.org/10.1016/j.apergo.2015.11.015

Ma Q, Chan AHS, Teh PL (2021) Insights into Older Adults’ Technology Acceptance through Meta-Analysis. Int J Hum-Comput Interact 37(11):1049–1062. https://doi.org/10.1080/10447318.2020.1865005

Macedo IM (2017) Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Comput Human Behav 75:935–948. https://doi.org/10.1016/j.chb.2017.06.013

Maidhof C, Offermann J, Ziefle M (2023) Eyes on privacy: acceptance of video-based AAL impacted by activities being filmed. Front Public Health 11:1186944. https://doi.org/10.3389/fpubh.2023.1186944

Majumder S, Aghayi E, Noferesti M, Memarzadeh-Tehran H, Mondal T, Pang Z, Deen MJ (2017) Smart homes for elderly healthcare—Recent advances and research challenges. Sensors 17(11):2496. https://doi.org/10.3390/s17112496

Article   ADS   PubMed   PubMed Central   Google Scholar  

Mhlanga D (2023) Artificial Intelligence in elderly care: Navigating ethical and responsible AI adoption for seniors. Available at SSRN 4675564. 4675564 min) Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process. Inf Process Manag 58(1):102428. https://doi.org/10.1016/j.ipm.2020.102428

Mitzner TL, Boron JB, Fausset CB, Adams AE, Charness N, Czaja SJ, Sharit J (2010) Older adults talk technology: Technology usage and attitudes. Comput Human Behav 26(6):1710–1721. https://doi.org/10.1016/j.chb.2010.06.020

Mitzner TL, Savla J, Boot WR, Sharit J, Charness N, Czaja SJ, Rogers WA (2019) Technology adoption by older adults: Findings from the PRISM trial. Gerontologist 59(1):34–44. https://doi.org/10.1093/geront/gny113

Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106:213–228. https://doi.org/10.1007/s11192-015-1765-5

Mostaghel R (2016) Innovation and technology for the elderly: Systematic literature review. J Bus Res 69(11):4896–4900. https://doi.org/10.1016/j.jbusres.2016.04.049

Mujirishvili T, Maidhof C, Florez-Revuelta F, Ziefle M, Richart-Martinez M, Cabrero-García J (2023) Acceptance and privacy perceptions toward video-based active and assisted living technologies: Scoping review. J Med Internet Res 25:e45297. https://doi.org/10.2196/45297

Naseri RNN, Azis SN, Abas N (2023) A Review of Technology Acceptance and Adoption Models in Consumer Study. FIRM J Manage Stud 8(2):188–199. https://doi.org/10.33021/firm.v8i2.4536

Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of Simulation & Gaming to the literature, 1970–2019: A bibliometric review. Simul Gaming 51(6):744–769. https://doi.org/10.1177/1046878120941569

Olmedo-Aguirre JO, Reyes-Campos J, Alor-Hernández G, Machorro-Cano I, Rodríguez-Mazahua L, Sánchez-Cervantes JL (2022) Remote healthcare for elderly people using wearables: A review. Biosensors 12(2):73. https://doi.org/10.3390/bios12020073

Pan S, Jordan-Marsh M (2010) Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput Human Behav 26(5):1111–1119. https://doi.org/10.1016/j.chb.2010.03.015

Pan X, Yan E, Cui M, Hua W (2018) Examining the usage, citation, and diffusion patterns of bibliometric map software: A comparative study of three tools. J Informetr 12(2):481–493. https://doi.org/10.1016/j.joi.2018.03.005

Park JS, Kim NR, Han EJ (2018) Analysis of trends in science and technology using keyword network analysis. J Korea Ind Inf Syst Res 23(2):63–73. https://doi.org/10.9723/jksiis.2018.23.2.063

Peek ST, Luijkx KG, Rijnaard MD, Nieboer ME, Van Der Voort CS, Aarts S, Wouters EJ (2016) Older adults’ reasons for using technology while aging in place. Gerontology 62(2):226–237. https://doi.org/10.1159/000430949

Peek ST, Luijkx KG, Vrijhoef HJ, Nieboer ME, Aarts S, van der Voort CS, Wouters EJ (2017) Origins and consequences of technology acquirement by independent-living seniors: Towards an integrative model. BMC Geriatr 17:1–18. https://doi.org/10.1186/s12877-017-0582-5

Peek ST, Wouters EJ, Van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJ (2014) Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform 83(4):235–248. https://doi.org/10.1016/j.ijmedinf.2014.01.004

Peek STM, Luijkx KG, Vrijhoef HJM, Nieboer ME, Aarts S, Van Der Voort CS, Wouters EJM (2019) Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: a dynamical framework. BMC Geriatr 19:1–13. https://doi.org/10.1186/s12877-019-1241-9

Perez AJ, Siddiqui F, Zeadally S, Lane D (2023) A review of IoT systems to enable independence for the elderly and disabled individuals. Internet Things 21:100653. https://doi.org/10.1016/j.iot.2022.100653

Piau A, Wild K, Mattek N, Kaye J (2019) Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res 21(8):e12785. https://doi.org/10.2196/12785

Pirzada P, Wilde A, Doherty GH, Harris-Birtill D (2022) Ethics and acceptance of smart homes for older adults. Inform Health Soc Care 47(1):10–37. https://doi.org/10.1080/17538157.2021.1923500

Pranckutė R (2021) Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 9(1):12. https://doi.org/10.3390/publications9010012

Qian K, Zhang Z, Yamamoto Y, Schuller BW (2021) Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring. IEEE Signal Process Mag 38(4):78–88. https://doi.org/10.1109/MSP.2021.3057298

Redner S (1998) How popular is your paper? An empirical study of the citation distribution. Eur Phys J B-Condens Matter Complex Syst 4(2):131–134. https://doi.org/10.1007/s100510050359

Sayago S (ed.) (2019) Perspectives on human-computer interaction research with older people. Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-030-06076-3

Schomakers EM, Ziefle M (2023) Privacy vs. security: trade-offs in the acceptance of smart technologies for aging-in-place. Int J Hum Comput Interact 39(5):1043–1058. https://doi.org/10.1080/10447318.2022.2078463

Schroeder T, Dodds L, Georgiou A, Gewald H, Siette J (2023) Older adults and new technology: Mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging 6(1):e44564. https://doi.org/10.2196/44564

Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K (2021) Application scenarios for artificial intelligence in nursing care: rapid review. J Med Internet Res 23(11):e26522. https://doi.org/10.2196/26522

Seuwou P, Banissi E, Ubakanma G (2016) User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security. In: Global Security, Safety and Sustainability-The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings 11:230-251. Springer International Publishing. https://doi.org/10.1007/978-3-319-51064-4_19

Shiau WL, Wang X, Zheng F (2023) What are the trend and core knowledge of information security? A citation and co-citation analysis. Inf Manag 60(3):103774. https://doi.org/10.1016/j.im.2023.103774

Sinha S, Verma A, Tiwari P (2021) Technology: Saving and enriching life during COVID-19. Front Psychol 12:647681. https://doi.org/10.3389/fpsyg.2021.647681

Soar J (2010) The potential of information and communication technologies to support ageing and independent living. Ann Telecommun 65:479–483. https://doi.org/10.1007/s12243-010-0167-1

Strotmann A, Zhao D (2012) Author name disambiguation: What difference does it make in author‐based citation analysis? J Am Soc Inf Sci Technol 63(9):1820–1833. https://doi.org/10.1002/asi.22695

Talukder MS, Sorwar G, Bao Y, Ahmed JU, Palash MAS (2020) Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technol Forecast Soc Change 150:119793. https://doi.org/10.1016/j.techfore.2019.119793

Taskin Z, Al U (2019) Natural language processing applications in library and information science. Online Inf Rev 43(4):676–690. https://doi.org/10.1108/oir-07-2018-0217

Touqeer H, Zaman S, Amin R, Hussain M, Al-Turjman F, Bilal M (2021) Smart home security: challenges, issues and solutions at different IoT layers. J Supercomput 77(12):14053–14089. https://doi.org/10.1007/s11227-021-03825-1

United Nations Department of Economic and Social Affairs (2023) World population ageing 2023: Highlights. https://www.un.org/zh/193220

Valk CAL, Lu Y, Randriambelonoro M, Jessen J (2018) Designing for technology acceptance of wearable and mobile technologies for senior citizen users. In: 21st DMI: Academic Design Management Conference (ADMC 2018), Design Management Institute, pp 1361–1373. https://www.dmi.org/page/ADMC2018

Van Eck N, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3

Vancea M, Solé-Casals J (2016) Population aging in the European Information Societies: towards a comprehensive research agenda in eHealth innovations for elderly. Aging Dis 7(4):526. https://doi.org/10.14336/AD.2015.1214

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

Wagner N, Hassanein K, Head M (2010) Computer use by older adults: A multi-disciplinary review. Comput Human Behav 26(5):870–882. https://doi.org/10.1016/j.chb.2010.03.029

Wahlroos N, Narsakka N, Stolt M, Suhonen R (2023) Physical environment maintaining independence and self-management of older people in long-term care settings—An integrative literature review. J Aging Environ 37(3):295–313. https://doi.org/10.1080/26892618.2022.2092927

Wang CL, Chen XJ, Yu T, Liu YD, Jing YH (2024a) Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11(1):1–17. https://doi.org/10.1057/s41599-024-02717-y

Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023a) Understanding the Continuance Intention of College Students Toward New E-learning Spaces Based on an Integrated Model of the TAM and TTF. Int J Hum-comput Int 1–14. https://doi.org/10.1080/10447318.2023.2291609

Wang CL, Wang HM, Li YY, Dai J, Gu XQ, Yu T (2024b) Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. Int J Hum-comput Int 1–23. https://doi.org/10.1080/10447318.2024.2383033

Wang J, Zhao W, Zhang Z, Liu X, Xie T, Wang L, Zhang Y (2024c) A journey of challenges and victories: a bibliometric worldview of nanomedicine since the 21st century. Adv Mater 36(15):2308915. https://doi.org/10.1002/adma.202308915

Wang J, Chen Y, Huo S, Mai L, Jia F (2023b) Research hotspots and trends of social robot interaction design: A bibliometric analysis. Sensors 23(23):9369. https://doi.org/10.3390/s23239369

Wang KH, Chen G, Chen HG (2017) A model of technology adoption by older adults. Soc Behav Personal 45(4):563–572. https://doi.org/10.2224/sbp.5778

Wang S, Bolling K, Mao W, Reichstadt J, Jeste D, Kim HC, Nebeker C (2019) Technology to Support Aging in Place: Older Adults’ Perspectives. Healthcare 7(2):60. https://doi.org/10.3390/healthcare7020060

Wang Z, Liu D, Sun Y, Pang X, Sun P, Lin F, Ren K (2022) A survey on IoT-enabled home automation systems: Attacks and defenses. IEEE Commun Surv Tutor 24(4):2292–2328. https://doi.org/10.1109/COMST.2022.3201557

Wilkowska W, Offermann J, Spinsante S, Poli A, Ziefle M (2022) Analyzing technology acceptance and perception of privacy in ambient assisted living for using sensor-based technologies. PloS One 17(7):e0269642. https://doi.org/10.1371/journal.pone.0269642

Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F (2021) Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health 21:1–12. https://doi.org/10.1186/s12889-021-11623-w

Xia YQ, Deng YL, Tao XY, Zhang SN, Wang CL (2024) Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework. Humanit Soc Sci Commun 11:266. https://doi.org/10.1057/s41599-024-02718-x

Xie H, Zhang Y, Duan K (2020) Evolutionary overview of urban expansion based on bibliometric analysis in Web of Science from 1990 to 2019. Habitat Int 95:102100. https://doi.org/10.1016/j.habitatint.2019.10210

Xu Z, Ge Z, Wang X, Skare M (2021) Bibliometric analysis of technology adoption literature published from 1997 to 2020. Technol Forecast Soc Change 170:120896. https://doi.org/10.1016/j.techfore.2021.120896

Yap YY, Tan SH, Choon SW (2022) Elderly’s intention to use technologies: a systematic literature review. Heliyon 8(1). https://doi.org/10.1016/j.heliyon.2022.e08765

Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390. https://doi.org/10.1057/s41599-023-01904-7

Yusif S, Soar J, Hafeez-Baig A (2016) Older people, assistive technologies, and the barriers to adoption: A systematic review. Int J Med Inform 94:112–116. https://doi.org/10.1016/j.ijmedinf.2016.07.004

Zhang J, Zhu L (2022) Citation recommendation using semantic representation of cited papers’ relations and content. Expert Syst Appl 187:115826. https://doi.org/10.1016/j.eswa.2021.115826

Zhao Y, Li J (2024) Opportunities and challenges of integrating artificial intelligence in China’s elderly care services. Sci Rep 14(1):9254. https://doi.org/10.1038/s41598-024-60067-w

Article   ADS   MathSciNet   PubMed   PubMed Central   Google Scholar  

Download references

Acknowledgements

This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

Author information

Authors and affiliations.

School of Art and Design, Shaanxi University of Science and Technology, Xi’an, China

Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

Department of Education Information Technology, Faculty of Education, East China Normal University, Shanghai, China

Chengliang Wang

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, XS, YW, CW; methodology, XS, ZL, CG, CW; software, XS, CG, YW; writing-original draft preparation, XS, CW; writing-review and editing, XS, CG, ZH, CW; supervision, ZL, ZH, CW; project administration, ZL, ZH, CW; funding acquisition, XS, CG. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.

Corresponding author

Correspondence to Chengliang Wang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

Ethical approval was not required as the study did not involve human participants.

Informed consent

Informed consent was not required as the study did not involve human participants.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

Download citation

Received : 20 June 2024

Accepted : 21 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1057/s41599-024-03658-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Social media use and depression in adolescents: a scoping review

There have been increases in adolescent depression and suicidal behaviour over the last two decades that coincide with the advent of social media (SM) (platforms that allow communication via digital media), which is widely used among adolescents. This scoping review examined the bi-directional association between the use of SM, specifically social networking sites (SNS), and depression and suicidality among adolescents. The studies reviewed yielded four main themes in SM use through thematic analysis: quantity of SM use, quality of SM use, social aspects associated with SM use, and disclosure of mental health symptoms. Research in this field would benefit from use of longitudinal designs, objective and timely measures of SM use, research on the mechanisms of the association between SM use and depression and suicidality, and research in clinical populations to inform clinical practice.

Introduction

Over the past several decades, adolescent depression and suicidal behaviours have increased considerably. In the USA, depression diagnoses among youth increased from 8.7% in 2005 to 11.3% in 2014 ( Mojtabai, Olfson, & Han, 2016 ). Additionally, suicide is the second leading cause of death among youth between the ages of 10 and 34 ( Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2017 ), with a 47.5% increase since 2000 ( Miron, Yu, Wilf-Miron, & Kohane, 2019 ). One suggested cause for this rise in adolescent depression and suicide is the advent of social media (SM) ( McCrae, Gettings, & Purssell, 2017 ; Twenge, Joiner, Rogers, & Martin, 2018 ).

The term ‘social media’ describes types of media that involve digital platforms and interactive participation. SM includes forms such as email, text, blogs, message boards, connection sites (online dating), games and entertainment, apps, and social networking sites (SNS) ( Manning, 2014 ). Over the past decade, SNS platforms designed to help people communicate and share information online have become ubiquitous. Among youth, 97% of all adolescents between the ages of 13 and 17 use at least one of the following seven SNS platforms: YouTube (85% of adolescents), Instagram (72%), Snapchat (69%), Facebook (51%), Twitter (32%), Tumblr (9%) or Reddit (7%) ( Pew Research Center, 2018a ).

Concerns have arisen around the effects of SM on adolescents’ mental health, due to SM’s association with decreased face-to-face interpersonal interactions ( Baym, 2010 ; Kraut et al., 1998 ; Nie, Hillygus, & Erbring, 2002 ; Robinson, Kestnbaum, Neustadtl, & Alvarez, 2002 ), addiction-like behaviours ( Anderson, Steen, & Stavropoulos, 2017 ), online bullying ( Kowalski, Limber, & Agatston, 2012 ), social pressure through increased social comparisons ( Guernsey, 2014 ), and contagion effect through increased exposure to suicide stories on SM ( Bell, 2014 ).

Conversely, others have described potential benefits of SM use in adolescents such as feelings of greater connection to friends and interactions with more diverse groups of people who can provide support ( Pew Research Center, 2018b ). In fact, higher internet use has been associated with positive social well-being, higher use of communication tools, and increased face-to-face conversations and social contacts in college students ( Baym, Zhang, & Lin, 2004 ; Kraut et al., 2002 ; Wang & Wellman, 2010 ). These findings suggest that internet use, including SM, may provide opportunities for social connection and access to information ( Reid Chassiakos et al., 2016 ).

Recent systematic reviews examining the association between online technologies and depression have found a ‘general correlation’ between SM use and depression in adolescents, but with conflicting findings in some domains (e.g. the association between time spent on SM and mental health problems), overall limited quality of the evidence ( Keles, McCrae, & Grealish, 2019 ), and a relative absence of studies designed to show causal effects ( Best, Manktelow, & Taylor, 2014 ). The scope of search in these reviews is broader in topic, including online technologies other than SM ( Best et al., 2014 ) or focussed on a select number of studies in order to meet the requirements of a systematic review ( Keles et al., 2019 ). With this scoping review, we aim to expand the inclusion of studies with a range of designs, while narrowing the scope of the topic of SM to those studies that specifically included SNS use. Additionally, we aim to expand the understanding and potential research gaps on the bi-directional association between SM and depression and suicidal behaviours in adolescents, including studies that consider SM use as a predictor as well as an outcome. A better understanding of this relationship can inform interventions and screenings related to SM use in clinical settings.

This scoping review was initiated by a research team including 3 mental health professionals with clinical expertise in treating depression and suicidality in adolescents. We followed the framework suggested by Arksey and O’Malley (2005) for scoping reviews. The review included five steps: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; and (5) collating, summarizing and reporting the results.

Research question

The review was guided by the question: What is known from the existing literature about the association between depression and suicidality and use of SNS among adolescents? Given that much of the literature used SM and SNS interchangeably, this review used the term ‘social media’ or ‘SM’ when it was difficult to discern if the authors were referring exclusively to SNS.

Data sources and search strategy

The team conceived the research question through a series of discussions, and the first author (CV) consulted an informationist to identify the appropriate search terms and databases. A search of the database PsychINFO limited to peer-reviewed articles was conducted on 5 June 2019 (see Table 1 for search strategy). No additional methods were identified through other sources. The search was broad to include articles measuring depression as an outcome variable, and as a co-variate or independent variable. There was no restriction on the type of study design included, and English and Spanish language articles were included in the search. Articles were organized using Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia).

Search strategy.

Eligibility criteria

(1) The study examined SM (versus internet use in general) and made specific mention of SNS; (2) participants were between the ages of 10 and 18. If adults were included, the majority of the study population was between 10–18 years of age, or the mean participant age was 18 or younger; (3) the study examined the association between SM use and depression and/or suicidality; (4) the study included at least one measure of depression; and (5) if the focus of the study was on SM addiction or cyberbullying, it included mention and a measure of depressive symptoms. We did not include articles in which: (1) the study primarily focussed on media use other than SM, or that did not specifically mention inclusion of SNS (e.g. studies that focussed only on TV, video game, smartphone use, blogging, email); (2) included primarily adult population; (3) was not an original study, but a case report, review, commentary, erratum, or letter to the editor; (4) focussed on addiction and cyberbullying exclusively without a depression measure; and (5) the method used was content analysis of SM posts without specification of the population age range.

Title and abstract relevance screening

The search yielded 728 articles of which six duplicates were removed. One author (CV) screened the remainder of the articles by title and abstract and a second author (TL) reviewed every 25th article for agreement. All authors screened full-text articles and extracted data from those that met the inclusion criteria. The authors met over the course of the full-text review process to resolve conflicts and maintain consistency among the authors themselves and with the research question. Of the total number of studies included for full-text review, 505 articles were excluded. Out of the 223 full-text studies assessed for eligibility, 175 were excluded. A total of 42 articles were eligible for review (see Figure 1 : PRISMA flow chart for details). A form was developed to extract the characteristics of each study that included author and year of publication, objectives of the study, study method, country where the study was conducted, depression scale used, number of participants, participant age, and results (see Table 2 for details).

PRISMA flow chart of data selection process.

Data charting form including author and year of publication, objectives of the study, method used, country where the study was conducted, depression scale used, number of participants, participant age, results and main social media focus.

AIU = Addictive internet Use; BIU = Borderline Addictive Internet Use; BSMAS = Bergen Social Media Addiction Scale; BIU = Borderline addictive internet use; CBP = Cyberbullying Perpetration; CERM = Cuestionario de Experiencias Relacionadas con el móvil (Questionnaire of Experiences Related to the cellphone); DIB = Dysfunctional Internet Behaviour; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders (4th edition, Text Revision); FOMO = Fear of Missing Out; HVSM = Highly Visual Social Media; SNI = Intensity of social network use; IA = Internet Addiction; IAB = Internet Addictive Behaviour; OSNA = Online social networking addiction; PSMU = Problematic Social Media Use; RADS-2 = Reynolds Adolescent Depression Scale - Version 2; SITBs = self-injurious thoughts and behaviours; SNS = social networking sites.

Data summary and synthesis

After reviewing the table, each study was labelled according to the main focus of research related to SM, based on the objectives, variables used, and results of the study. The topics were classified into nine different categories based on the main SM focus of the article; categories were discussed and reviewed by two authors (TL and CV) ( Table 2 ). All authors then discussed the categories and grouped them into four main themes of studies looking at SM and depression in adolescents.

A total of 42 studies published between 2011 and 2019 met the inclusion criteria. Of the studies included, 16 were conducted in European Countries, 14 in the USA, 5 in Asia, 3 in Canada, 2 in Australia, and 2 in Latin American Countries. The number of participants per study ranged from 23 in a qualitative study (94 in the smallest quantitative study) to 118,545 participants in the largest study ( Table 2 ).

The studies reviewed were grouped into four themes with nine categories according to the main focus of the research. The themes and categories were: (1) quantity of SNS use: effects of the frequency of SM use and problematic SM use (or evidence of addictive engagement with SM); (2) quality of SM use: characteristics of SNS use and social comparisons; (3) social aspects of SM use: cyberbullying, social support, and parental involvement; and (4) disclosure of mental health symptoms: online disclosure and prediction of symptoms and suicide contagion effect ( Figure 2 ).

Number of studies by theme (quantity, quality, social and disclosure) and time period (2011–2012, 2013–2014, 2015–2016 and 2017–2018).

Quantity of SM use

The majority of studies ( n = 24) examined quantity of SM use by measuring either frequency or time spent on SM ( n = 17), or problematic or addictive engagement with SM ( n = 7).

Frequency of use

The majority of studies found a positive correlation between time spent on SNS and higher levels of The majority of studies found a positive correlation between time spent on SNS and higher levels of depression ( Akkın Gürbüz, Demir, Gökalp Özcan, Kadak, & Poyraz, 2017 ; Marengo, Longobardi, Fabris & Settanni, 2018 ; Pantic et al., 2012 ; Twenge et al., 2018 ; Woods & Scott, 2016 ). Higher frequency of SM use (≥2 h a day) was also found to be positively associated with suicidal ideation ( Sampasa-Kanyinga & Lewis, 2015 ) and attempts ( Sampasa-Kanyinga & Hamilton, 2015 ), in addition to deficits in self-regulation ( Lee, Ho, & Lwin, 2017 ). Factors such as the number of SM accounts and the frequency of checking SM ( Barry, Sidoti, Briggs, Reiter, & Lindsey, 2017 ) were associated with a variety of symptoms, including depression.

A study ( Oberst, Wegmann, Stodt, Brand, & Chamarro, 2017 ) examining SM use as an outcome suggested that depression may affect SM use both directly, and indirectly, mediated by the Fear of Missing Out (or the apprehension of missing rewarding experiences that others might be enjoying) ( Przybylski, Murayama, DeHaan, & Gladwell, 2013 ). Adolescents with depression were also found to have more difficulty regulating their SM use ( Lee et al., 2017 ).

Longitudinal studies suggested a reciprocal relationship between quantity of SM use and depression. Frison and Eggermont (2017) found that frequency of Instagram browsing at baseline predicted depressed mood six months later and depressed mood at baseline predicted later frequency of photo posting. Additionally, heavy use (>4 h per day) of the internet to communicate (including social networking) and play games (gaming) predicted depressive symptoms a year later ( Romer, Bagdasarov, & More, 2013 ). Further, depressive symptoms predicted increased internet use and decreased participation in non-screen activities (e.g. sports). Finally, Salmela-Aro, Upadyaya, Hakkarainen, Lonka, and Alho (2017) found that school burnout increased the risk for later excessive internet use and depressive symptoms. Conversely, Houghton et al. (2018) found small, positive bi-directional associations between depressive symptoms and screen use 1 year later, but their final model did not support a longitudinal association.

Yet, not all studies found a positive association between frequency of use and depressed mood. While Blomfield-Neira and Barber (2014) reported a link between adolescents having a SM profile and depressed mood, they found no correlation between SM frequency of use and depressed mood. Rather, investment in SM (a measure of how important SM is to an adolescent) was linked to poorer adjustment, lower self-esteem and depressed mood. Moderate SM use (a stable trend in the time spent on SM during adolescence and into early adulthood that did not interfere with functioning) was associated with better emotional self-regulation ( Coyne, Padilla-Walker, Holmgren, & Stockdale, 2018 ) and healthier development, especially when used to acquire information ( Romer et al., 2013 ). Finally, Rodriguez Puentes and Parra (2014) found a positive association between SM and externalizing behaviours, but no significant association between SM use and depression.

Additionally, age moderated the effects of frequency of use on depression. For example, in one study, older adolescents with higher SM use had higher ‘offline’ social competence, while younger adolescents with higher SM use had more internalizing problems and diminished academics and activities ( Tsitsika, Janikian, et al., 2014 ).

Problematic SM use

Seven studies explored problematic use or engagement with SM or the internet in an addictive manner (a dysfunctional pattern of behaviour similar to that of impulse control disorders, which causes distress and/or functional impairment) ( Critselis et al., 2014 ).

An addiction-like pattern of internet use (including SM use) was associated with emotional maladjustment ( Critselis et al., 2014 ), internalizing and externalizing symptoms ( Tsitsika, Tzavela, et al., 2014 ), and depressive mood ( Van Rooij, Ferguson, Van de Mheen, & Schoenmakers, 2017 ). Further, depressive mood predicted problematic internet use (both SM and gaming, independently) ( Kırcaburun et al., 2018 ; Van Rooij et al., 2017 ).

Bányai et al. (2017) assessed the prevalence of problematic internet use conducting a latent profile analysis to describe classes of users and found that the class described as ‘at risk’ for problematic internet and SM use tended to be female, use the internet for longer periods, and have lower self-esteem and more depressive symptoms. Yet, while Banjanin, Banjanin, Dimitrijevic, and Pantic (2015) found a positive correlation between internet addiction and depression in high school students (particularly for females), no such correlation was found with engagement with SM (measured by number of pictures posted).

Several studies examined mediators of the association of problematic SM use and depression. Wang et al. (2018) found that rumination mediated the relationship between SM addiction and adolescent depression, with a stronger effect among adolescents with low self-esteem. Additionally, insomnia partially mediated the association between SM addiction and depressive symptoms ( Li et al., 2017 ). Woods and Scott (2016) found that nighttime-specific SM use (in addition to overall use and emotional investment in SM) was associated with poorer sleep quality, anxiety and depressive symptoms. Finally, problematic SM use mediated the association between depressive symptoms and cyberbullying perpetration ( Kırcaburun et al., 2018 ).

Quality of SNS use

In addition to the frequency of adolescents’ engagement with SM, another focus of research has been the ways in which adolescents engage with SM. Of the studies selected, four primarily examined engagement styles with SM and two specifically examined social comparisons with other users.

Characteristics of SM use

The ways in which adolescents use SM may also have an effect on depression. One study ( Frison & Eggermont, 2016 ) characterized SM use as public (e.g. updating one’s status on a profile) vs private (e.g. messaging), and active (e.g. interacting with others on SM) vs passive (e.g. browsing on SM) and found that public Facebook use was associated with adolescent depressed mood. Among girls, passive use of Facebook yielded negative outcomes such as depressed mood, while active use yielded positive outcomes such as perceived social support ( Frison & Eggermont, 2016 ). A longitudinal study of Flemish adolescents by the same group ( Frison & Eggermont, 2017 ) found passive SM use at baseline to predict depressive symptoms 7 months later, while depressive symptoms predicted active use of SM. Interestingly, there was no association between depressive symptoms and Facebook use (frequency of use, network size, self-presentation, and peer interaction) in a study conducted among healthy adolescents ( Morin-Major et al., 2016 ).

Romer et al. (2013) found that the types of internet activities utilized (e.g. SNS, blogs, etc.) were associated with the frequency of self-reported depression-like symptoms. Additionally, using the internet for information searching was associated with higher grades, more frequent participation in clubs, and lower reports of depressive symptoms, while using the internet more than 4 h per day to communicate or play games was associated with greater depression-like symptoms, suggesting that Internet use for acquiring information is associated with healthy development.

A qualitative study further explored positive and negative aspects of SM use among adolescents diagnosed with clinical depression ( Radovic, Gmelin, Stein, & Miller, 2017 ). Participants described positive SM use as including searching for positive content (e.g. entertainment, humour, content creation) or social connection, while they described negative SM use as sharing risky behaviours, cyberbullying, or making self-denigrating comparisons with others. Furthermore, this study found that adolescents’ use of SM shifted from negative to positive during the course of treatment.

Social comparisons

Two studies examined social comparisons made through SM and the association with depression. Nesi and Prinstein (2015) found that technology-based social comparison and feedback-seeking were associated with depressive symptoms, even when controlling for the effects of overall frequency of technology use, offline excessive reassurance-seeking, and prior depressive symptoms. This association was strongest among females and adolescents low in popularity (as measured by peer report). Niu et al. (2018) found that negative social comparisons mediated the association between Qzone use (a Chinese SM site) and depression, and that the association between Qzone use and negative social comparisons was stronger among individuals with low self-esteem. However, there was no direct effect of Qzone use on depression. An additional study that primarily focussed on studying frequency of use ( Marengo et al., 2018 ) found that increased use of highly visual SM (e.g. Instagram) predicted internalizing symptoms and body image concerns in a student sample. Moreover, in this study, the effect of highly visual SM on internalizing symptoms was mediated by body image concerns.

Social aspects of SM use

Several studies looked at the social aspects of engagement with SM, either by evaluating the effects of cybervictimization ( n = 4) on depression, parental involvement both through monitoring of SM use or direct engagement with the adolescent ( n = 3), and aspects of social support received by the adolescent within and outside of SNS ( n = 2).

Cyberbullying/cybervictimization

Four studies examined cyberbullying via SM and depressive symptoms. Duarte, Pittman, Thorsen, Cunningham, and Ranney (2018) found that symptoms of depression, post-traumatic stress disorder, and suicidal ideation were more prevalent among participants who reported any past-year cyberbullying (either victimization or perpetration). After adjusting for a range of demographic factors, only lesbian, gay, and bisexual status correlated with cyberbullying involvement or adverse mental health outcomes. Another study found that cyberbullying victimization fully mediated the association between SM use and psychological distress and suicide attempts ( Sampasa-Kanyinga & Hamilton, 2015 ). Furthermore, a 12-month longitudinal study found that cybervictimization predicted later depressive symptoms ( Cole et al., 2016 ). Depressive symptoms have also been shown to be a risk factor (rather than an outcome) for cybervictimization on Facebook ( Frison, Subrahmanyam, & Eggermont, 2016 ), showing evidence of the bi-directionality of this association.

Social support

While many studies examined potential negative effects of SM use, some studies examined the positive effects of SM use on youth outcomes, including social support. Frison and Eggermont (2015) found that adolescents seeking social support through Facebook had improved depressive symptoms if support was received, but worsened symptoms if support was not received. This pattern was not found in non-virtual social support contexts, suggesting differences in online and traditional social support contexts. A later study that primarily focussed on the characteristics of SM use ( Frison & Eggermont, 2016 ) found that perception of online support was particularly protective against depressive symptoms in girls with ‘active’ Facebook use (e.g. those who update their status or instant message on Facebook). Finally, Frison et al. (2016) showed that support from friends can be a protective factor of Facebook victimization.

Parental involvement/parental monitoring

Studies examining parent and family role in adolescent SM use and its outcomes were heterogeneous. One study ( Coyne, Padilla-Walker, Day, Harper, & Stockdale, 2014 ) explored adolescent use of SM with parents and found lower internalizing behaviours in participants who used SNS with their parents (mediated by feelings of parent/child connection). Another study ( Fardouly, Magson, Johnco, Oar, & Rapee, 2018 ) examined parent control over preadolescents’ time spent on SM and found no association between parental control and preadolescent depressive symptoms.

Family relationships offline were also associated with adolescent outcomes. Isarabhakdi and Pewnil (2016) examined adolescents’ engagement with offline relationships and found improved mental health outcomes with higher involvement in family activities and with peers, while internet use did not significantly improve mental well-being. This finding suggests that in-person support systems were more effective for the promotion of mental well-being. Interestingly, in Szwedo, Mikami, and Allen (2011) , negative interactions with mothers during early adolescence were associated with youth preferring online versus face-to-face communication, experiencing more negative interactions on webpages, and forming close friendships with someone they met online 7 years later. An additional study that primarily focussed on suicide contagion ( Tseng & Yang, 2015 ) found that family support was protective for both males and females, while friend support was protective only for females. However, ‘significant other’ support was a risk factor for suicidal plans among females.

Disclosure of mental health symptoms on SM

A few of the studies selected focussed on studying the disclosure of depressive symptoms on SM and explored the potential of disclosure of symptoms of distress on SM to predict depression and suicide, in addition to the phenomenon of suicide contagion.

Online disclosure and prediction of mental health symptoms

Although content analysis is a method theorized to have potential to predict and prevent non-suicidal and suicidal self-injurious behaviours, the data are mixed. Ophir, Asterhan, and Schwarz (2019) examined the predictive validity of explicit references to personal distress in adolescents’ Facebook postings, comparing these postings with external, self-report measures of psychological distress (e.g. depression) and found that most depressed adolescents did not publish explicit references to depression. Additionally, adolescents published less verbal content than adult users of SNS. Conversely, Akkın Gürbüz et al. (2017) found that while disclosures of depressed mood were frequent among both depressed and non-depressed adolescents, those who were depressed shared more negative feelings, anhedonia, and suicidal thoughts on SM than those who were not depressed.

Suicide contagion effect

One longitudinal study examined suicide contagion effects ( Dunlop, More, & Romer, 2011 ) finding that even though traditional SNS (e.g. Facebook or MySpace) were a significant source of exposure to suicide stories, this exposure was not associated with increases in suicidal ideation one year later. On the other hand, exposure to online discussion forums (including self-help forums) did predict increases in suicidal ideation over time. Notably, this study found that in a quarter of the sample, the exposure to suicide stories took place through SM. Another study ( Tseng & Yang, 2015 ) found that higher importance attributed to web communication (e.g. chatting or making friends online) was associated with increased risk of self-injurious thoughts and behaviours in boys.

The recent rise in the prevalence of depression and suicide among adolescents has coincided with an increase in screen-related activities, including SM use ( Twenge et al., 2018 ), sparking an interest in investigating the effects of SM use on adolescent mental health. This interest has given rise to a broad scope of research, ranging from observational to experimental and qualitative studies through interviews or analysis of SM content, and systematic studies. This scoping review aimed to understand the breadth of research in the area of depression and SM (with a focus on SNS) and to identify the existing research gaps.

We identified four main themes of research, including (1) the quantity of SM use; (2) the quality of SM use; (3) social aspects associated with SM use; and (4) SM as a tool for disclosure of mental health symptoms and potential for prediction and prevention of depression and suicide outcomes.

Most research on SM and depressive symptoms has focussed on the effects of frequency of SM use and problematic SM use. The majority of articles included in this review demonstrated a positive and bi-directional association between frequency of SM use and depression and in some instances even suicidality. Yet some questions remain to be determined, including to what degree adolescents’ personal vulnerabilities and characteristics of SM use moderate the association between SM use and depression or suicidality, and whether other environmental factors, such as family support and/or monitoring, or cultural differences influence this association. Although moderate SM use may be associated with better self-regulation, it is unclear if this is due to moderate users being better at self-regulation.

Findings from the studies examining problematic SM use were consistent with prior studies linking problematic internet use with a variety of psychosocial outcomes including depressive symptoms ( Reid Chassiakos et al., 2016 ). Though limited in number, studies reviewed here suggested that problematic or addictive SM use may be more common in females ( Banyai et al., 2017 ; Kırcaburun et al., 2018 ) and in those starting use at a younger age ( Tsitsika, Janikian, et al., 2014 ). These findings suggest a possible role of screening for addictive SM use, with a particular focus on risk stratification for younger and female adolescents.

With respect to the effects of patterns and types of SM use, studies reviewed here suggest possible differential effects between passive and active, and private versus public SM use. This suggests that screening only for time spent on SM may be insufficient. Moreover, though there are types of SM use that have adverse mental health effects for adolescents (e.g. addictive patterns, nighttime use), other types of SM use, such as for information searching or receiving social support, may have a positive effect ( Coyne et al., 2018 ; Frison & Eggermont, 2016 ; Romer et al., 2013 ). Furthermore, over time, depressed adolescents can successfully shift their use of SM from negative (e.g. cyberbullying) to positive (e.g. searching for humour), possibly through increasing awareness of the effect of SM use on their mood ( Radovic et al., 2017 ). Given the ubiquity of SM use, these results suggest that interventions targeting changes in adolescents’ use of SM may be fruitful in improving their mental health.

Consistent with prior research ( Feinstein et al., 2013 ), studies examining social comparisons found significant associations between social comparisons made via SM and depression. The tendency of individuals to share more positive depictions of themselves on SM ( Subrahmanyam & Greenfield, 2008 ), and the increased opportunities for comparisons ( Steers, Wickham, & Acitelli, 2014 ) may suggest a confluence of risks for depression and an important avenue for interventions. Moreover, the studies reviewed and previous findings ( Buunk & Gibbons, 2007 ) suggest that individuals with low self-esteem may be at higher risk for the negative effects of social comparisons on mental health.

As previously shown ( Cénat et al., 2014 ), most studies found cyberbullying (either perpetration or victimization) was either associated with mental health problems ( Cole et al., 2016 ; Duarte et al., 2018 ) or moderated the relationship between SM use and depression and suicidality ( Sampasa-Kanyinga & Hamilton, 2015 ). Additionally, cyberbullying may be a distinctive form of victimization that requires further investigation in order to understand its impact on adolescent mental health ( Dempsey, Sulkowski, Nichols, & Storch, 2009 ).

Studies examining social support highlight the association of both depressed mood and low in-person social support with social networking and online support-seeking ( Frison & Eggermont, 2015 ). Moreover, while social support online can be beneficial ( Frison & Eggermont, 2015 ), excessive reliance on online communication and support may be problematic ( Twenge et al., 2018 ). Of note, parental involvement both positively and negatively affected SM use and adolescent outcomes. These mixed findings suggest a need to include parental relationships in research (both via online and ‘offline’ communication), to better understand their role in adolescents’ SM use and depression.

Surprisingly, depressed adolescents were not more likely to publish explicit references to depression on SM platforms than their healthy peers ( Ophir et al., 2019 ) which suggests that screening for depression via SM may not be useful when used alone. However, some depressed adolescents posted more negative feelings, anhedonia and suicidal ideation ( Akkın Gürbüz et al., 2017 ), suggesting that SM may be used as a supplemental tool to track the course of depressive mood over time and start discussions about mental health.

Suicide contagion effect is a relatively understudied area, despite concerns raised that increased exposure to SM may amplify this effect ( Bell, 2014 ). Given that adolescents are particularly vulnerable to the group contagion effect of suicide ( Stack, 2003 ) and the potential for increased exposure to suicide stories online ( Dunlop et al., 2011 ), interventions to limit this exposure could decrease suicide contagion.

The studies reviewed identified several potential moderators of the association between SM use and adolescent depression, including age and gender. The differential effects of SM use on mental health depending on the age of the adolescent ( Tsitsika, Tzavela, et al., 2014 ) are not surprising given the developmental differences in social and mood regulation skills between younger and older adolescents. Likewise, potential mediators of the effects of SM on mental health such as social comparisons ( Niu et al., 2018 ), body image concerns ( Marengo et al., 2018 ), perceived support online ( Frison & Eggermont, 2015 ), and parent–child relationship ( Coyne et al., 2014 ) may also be important targets for future interventions.

The studies reviewed present several limitations. Most studies were cross-sectional and could not elucidate the directionality of the association between SM use and depression. Most of the studies included self-report rather than clinician-administered measures of depression, and retrospective reports, asking participants to report on past activities. Newer methods that measure actual (and not just reported) use (e.g. news feed activity, number of likes and comments) and more frequent and timely reports of SM use (e.g. diaries) could more accurately explain these associations. Another limitation is that many of the studies recruited participants in schools, limiting the generalizability to clinical samples. It is possible that those students not in school were spending more time on SM and/or experiencing more depressive symptoms. Most studies included general assessments of SM without specifying whether the use was limited to SNS or other forms of SM or internet use. While we tried to narrow our search to studies that explicitly included questions on SNS use, many also asked about other types of SM use. Separating the different types of SM use may be difficult when asking for adolescents’ self-reports, but more immediate measures of mood symptoms and SNS use could be more specific and informative. Finally, while some studies included contextual factors such as the educational and family environments, other contextual factors such as ethnicity and cultural context are areas of potential for investigation.

Conclusions

In summary, extensive research on the quantity and quality of SM use has shown an association between SM use and depression in adolescents. Given that most studies are cross-sectional, longitudinal research would help assess the direction of this association. At the same time, some aspects of SM use may have a beneficial effect on adolescent well-being, such as the ability to have diversity of friendships and easily accessed supports. Furthermore, the use of SM content to detect symptoms has potential in depression and suicide prevention. Finally, moderators of the association between SM and adolescent depression and suicidality (e.g. gender, age, parental involvement) are areas to explore that would allow more targeted interventions. Since SM will remain an important facet of adolescents’ lives, a better understanding of the mechanisms of its relationship with depression could be beneficial to increase exposure to mental health interventions and promote well-being.

Acknowledgements

The authors acknowledge the help of Jaime Blanck, MLIS, MPA for her help with the search and retrieval of full-text articles.

Disclosure statement

Dr. Vidal is supported by the Stravos Niarchos Foundation. Ms. Lhaksampa and Dr. Miller are supported by the Once Upon a Time Foundation. Drs. Miller and Dr. Platt are supported by the Patient-Centered Outcomes Research Institute (PCORI). Dr. Platt is supported by the NIMH 1K23MH118431 and the Robert Wood Johnson Foundation.

• Akkın Gürbüz HG, Demir T, Gökalp Özcan B, Kadak MT, & Poyraz BC (2017). Use of social network sites among depressed adolescents . Behaviour & Information Technology , 36 , 517–523. doi: 10.1080/0144929X.2016.1262898 [ CrossRef ] [ Google Scholar ]
• Anderson EL, Steen E, & Stavropoulos V (2017). Internet use and problematic internet use: A systematic review of longitudinal research trends in adolescence and emergent adulthood . International Journal of Adolescence and Youth , 22 , 430–454. doi: 10.1080/02673843.2016.1227716 [ CrossRef ] [ Google Scholar ]
• Arksey H, & O’Malley L (2005). Scoping studies: Towards a methodological framework . International Journal of Social Research Methodology , 8 ( 1 ), 19–32. doi: 10.1080/1364557032000119616 [ CrossRef ] [ Google Scholar ]
• Banjanin N, Banjanin N, Dimitrijevic I, & Pantic I (2015). Relationship between internet use and depression: Focus on physiological mood oscillations, social networking and online addictive behavior . Computers in Human Behavior , 43 , 308–312. doi: 10.1016/j.chb.2014.11.013 [ CrossRef ] [ Google Scholar ]
• Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, … Demetrovics Z (2017). Problematic social media use: Results from a large-scale nationally representative adolescent sample . PLoS One , 12 ( 1 ), e0169839. doi: 10.1371/journal.pone.0169839 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barry CT, Sidoti CL, Briggs SM, Reiter SR, & Lindsey RA (2017). Adolescent social media use and mental health from adolescent and parent perspectives . Journal of Adolescence , 61 , 1–11. doi: 10.1016/j.adolescence.2017.08.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baym NK (2010). Personal connections in the digital age . Cambridge: Polity Press. [ Google Scholar ]
• Baym NK, Zhang YB, & Lin MC (2004). Social interactions across media: Interpersonal communication on the internet, telephone and face-to-face . New Media & Society , 6 , 299–318. doi: 10.1177/1461444804041438 [ CrossRef ] [ Google Scholar ]
• Bell J (2014). Harmful or helpful? The role of the Internet in self-harming and suicidal behaviour in young people . Mental Health Review Journal , 19 ( 1 ), 61–71. doi: 10.1108/MHRJ-05-2013-0019 [ CrossRef ] [ Google Scholar ]
• Best P, Manktelow R, & Taylor B (2014). Online communication, social media and adolescent wellbeing: A systematic narrative review . Children and Youth Services Review , 41 , 27–36. doi: 10.1016/j.childyouth.2014.03.001 [ CrossRef ] [ Google Scholar ]
• Blomfield-Neira CJ, & Barber BL (2014). Social networking site use: Linked to adolescents’ social self-concept, self-esteem, and depressed mood . Australian Journal of Psychology , 66 ( 1 ), 56–64. doi: 10.1111/ajpy.12034 [ CrossRef ] [ Google Scholar ]
• Buunk AP, & Gibbons FX (2007). Social comparison: The end of a theory and the emergence of a field . Organizational Behavior and Human Decision Processes , 102 ( 1 ), 3–21. doi: 10.1016/j.obhdp.2006.09.007 [ CrossRef ] [ Google Scholar ]
• Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. (2017). Web-based Injury Statistics Query and Reporting System (WISQARS) [online] . Retrieved from www.cdc.gov/injury/wisqars . Accessed July 19, 2019.
• Cénat JM, Hébert M, Blais M, Lavoie F, Guerrier M, & Derivois D (2014). Cyberbullying, psychological distress and self-esteem among youth in Quebec schools . Journal of Affective Disorders , 169 , 7–9. doi: 10.1016/j.jad.2014.07.019 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cole DA, Zelkowitz RL, Nick E, Martin NC, Roeder KM, Sinclair-McBride K, & Spinelli T (2016). Longitudinal and incremental relation of cybervictimization to negative self-cognitions and depressive symptoms in young adolescents . Journal of Abnormal Child Psychology , 44 , 1321–1332. doi: 10.1007/s10802-015-0123-7 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Coyne SM, Padilla-Walker LM, Day RD, Harper J, & Stockdale L (2014). A friend request from dear old dad: Associations between parent–child social networking and adolescent outcomes . Cyberpsychology, Behavior, and Social Networking , 17 ( 1 ), 8–13. doi: 10.1089/cyber.2012.0623 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Coyne SM, Padilla-Walker LM, Holmgren HG, & Stockdale LA (2018). Instagrowth: A longitudinal growth mixture model of social media time use across adolescence . Journal of Research on Adolescence , 29 , 897–907. doi: 10.1111/jora.12424 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Critselis E, Janikian M, Paleomilitou N, Oikonomou D, Kassinopoulos M, Kormas G, & Tsitsika A (2014). Predictive factors and psychosocial effects of Internet addictive behaviors in Cypriot adolescents . International Journal of Adolescent Medicine and Health , 26 , 369–375. doi: 10.1515/ijamh-2013-0313 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dempsey AG, Sulkowski ML, Nichols R, & Storch EA (2009). Differences between peer victimization in cyber and physical settings and associated psychosocial adjustment in early adolescence . Psychology in the Schools , 46 , 962–972. doi: 10.1002/pits.20437 [ CrossRef ] [ Google Scholar ]
• Duarte C, Pittman SK, Thorsen MM, Cunningham RM, & Ranney ML (2018). Correlation of minority status, cyberbullying, and mental health: A cross-sectional study of 1031 adolescents . Journal of Child & Adolescent Trauma , 11 ( 1 ), 39–48. doi: 10.1007/s40653-018-0201-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dunlop SM, More E, & Romer D (2011). Where do youth learn about suicides on the Internet, and what influence does this have on suicidal ideation . Journal of Child Psychology and Psychiatry , 52 , 1073–1080. doi: 10.1111/j.1469-7610.2011.02416.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fardouly J, Magson NR, Johnco CJ, Oar EL, & Rapee RM (2018). Parental control of the time preadolescents spend on social media: Links with preadolescents’ social media appearance comparisons and mental health . Journal of Youth and Adolescence , 47 , 1456–1468. doi: 10.1007/s10964-018-0870-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Feinstein BA, Hershenberg R, Bhatia V, Latack JA, Meuwly N, & Davila J (2013). Negative social comparison on Facebook and depressive symptoms: Rumination as a mechanism . Psychology of Popular Media Culture , 2 , 161–110. doi: 10.1037/a0033111 [ CrossRef ] [ Google Scholar ]
• Frison E, & Eggermont S (2015). The impact of daily stress on adolescents’ depressed mood: The role of social support seeking through Facebook . Computers in Human Behavior , 44 , 315–325. doi: 10.1016/j.chb.2014.11.070 [ CrossRef ] [ Google Scholar ]
• Frison E, & Eggermont S (2016). Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood . Social Science Computer Review , 34 , 153–171. doi: 10.1177/0894439314567449 [ CrossRef ] [ Google Scholar ]
• Frison E, & Eggermont S (2017). Browsing, posting, and liking on Instagram: The reciprocal relationships between different types of Instagram use and adolescents’ depressed mood . Cyberpsychology, Behavior, and Social Networking , 20 , 603–609. doi: 10.1089/cyber.2017.0156 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Frison E, Subrahmanyam K, & Eggermont S (2016). The short-term longitudinal and reciprocal relations between peer victimization on Facebook and adolescents’ well-being . Journal of Youth and Adolescence , 45 , 1755–1771. doi: 10.1007/s10964-016-0436-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Guernsey L (2014). Garbled in translation: Getting media research to the press and public . Journal of Children and Media , 8 ( 1 ), 87–94. doi: 10.1080/17482798.2014.863486 [ CrossRef ] [ Google Scholar ]
• Houghton S, Lawrence D, Hunter SC, Rosenberg M, Zadow C, Wood L, & Shilton T (2018). Reciprocal relationships between trajectories of depressive symptoms and screen media use during adolescence . Journal of Youth and Adolescence , 47 , 2453–2467. doi: 10.1007/s10964-018-0901-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Isarabhakdi P, & Pewnil T (2016). Engagement with family, peers, and Internet use and its effect on mental well-being among high school students in Kanchanaburi Province , Thailand. International Journal of Adolescence and Youth , 21 ( 1 ), 15–26. doi: 10.1080/02673843.2015.1024698 [ CrossRef ] [ Google Scholar ]
• Keles B, McCrae N, & Grealish A (2019). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents . International Journal of Adolescence and Youth , 25 , 79–93. doi: 10.1080/02673843.2019.1590851 [ CrossRef ] [ Google Scholar ]
• Kırcaburun K, Kokkinos CM, Demetrovics Z, Király O, Griffiths MD, & Çolak TS (2018). Problematic online behaviors among adolescents and emerging adults: Associations between cyberbullying perpetration, problematic social media use, and psychosocial factors . International Journal of Mental Health and Addiction , 17 , 891–908. doi: 10.1007/s11469-018-9894-8 [ CrossRef ] [ Google Scholar ]
• Kowalski RM, Limber S, & Agatston PW (2012). Cyberbullying: Bullying in the digital age (2nd ed.). Malden, MA: Wiley-Blackwell. [ Google Scholar ]
• Kraut R, Kiesler S, Boneva B, Cummings J, Helgeson V, & Crawford A (2002). Internet paradox revisited . Journal of Social Issues , 58 ( 1 ), 49–74. doi: 10.1111/1540-4560.00248 [ CrossRef ] [ Google Scholar ]
• Kraut R, Patterson M, Lundmark V, Kiesler S, Mukophadhyay T, & Scherlis W (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist , 53 , 1017–1031. doi: 10.1037/0003-066X.53.9.1017 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lee EW, Ho SS, & Lwin MO (2017). Extending the social cognitive model—Examining the external and personal antecedents of social network sites use among Singaporean adolescents . Computers in Human Behavior , 67 , 240–251. doi: 10.1016/j.chb.2016.10.030 [ CrossRef ] [ Google Scholar ]
• Li JB, Lau JT, Mo PK, Su XF, Tang J, Qin ZG, & Gross DL (2017). Insomnia partially mediated the association between problematic Internet use and depression among secondary school students in China . Journal of Behavioral Addictions , 6 , 554–563. doi: 10.1556/2006.6.2017.085 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Manning J (2014). Social media, definition and classes of In Harvey K (Ed.), Encyclopedia of social media and politics (pp. 1158–1162). Thousand Oaks, CA: Sage. [ Google Scholar ]
• Marengo D, Longobardi C, Fabris MA, & Settanni M (2018). Highly-visual social media and internalizing symptoms in adolescence: The mediating role of body image concerns . Computers in Human Behavior , 82 , 63–69. doi: 10.1016/j.chb.2018.01.003 [ CrossRef ] [ Google Scholar ]
• McCrae N, Gettings S, & Purssell E (2017). Social media and depressive symptoms in childhood and adolescence: A systematic review . Adolescent Research Review , 2 , 315–330. doi: 10.1007/s40894-017-0053-4 [ CrossRef ] [ Google Scholar ]
• Miron O, Yu K, Wilf-Miron R, & Kohane IS (2019). Suicide rates among adolescents and young adults in the United States, 2000–2017 . JAMA , 321 , 2362–2364. doi: 10.1001/jama.2019.5054 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mojtabai R, Olfson M, & Han B (2016). National trends in the prevalence and treatment of depression in adolescents and young adults . Pediatrics , 138 , e20161878. doi: 10.1542/peds.2016-1878 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morin-Major JK, Marin MF, Durand N, Wan N, Juster RP, & Lupien SJ (2016). Facebook behaviors associated with diurnal cortisol in adolescents: Is befriending stressful? Psychoneuroendocrinology , 63 , 238–246. doi: 10.1016/j.psyneuen.2015.10.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nesi J, & Prinstein MJ (2015). Using social media for social comparison and feedback-seeking: Gender and popularity moderate associations with depressive symptoms . Journal of Abnormal Child Psychology , 43 , 1427–1438. doi: 10.1007/s10802-015-0020-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nie NH, Hillygus DS, & Erbring L (2002). Internet use, interpersonal relations, and sociability: A time diary study In Wellman B & Haythornthwaite C (Eds.), The Internet in everyday life (pp. 215–243). Malden, MA: Blackwell. [ Google Scholar ]
• Niu GF, Luo YJ, Sun XJ, Zhou ZK, Yu F, Yang SL, & Zhao L (2018). Qzone use and depression among Chinese adolescents: A moderated mediation model . Journal of Affective Disorders , 231 , 58–62. doi: 10.1016/j.jad.2018.01.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Oberst U, Wegmann E, Stodt B, Brand M, & Chamarro A (2017). Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out . Journal of Adolescence , 55 , 51–60. doi: 10.1016/j.adolescence.2016.12.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ophir Y, Asterhan CS, & Schwarz BB (2019). The digital footprints of adolescent depression, social rejection and victimization of bullying on Facebook . Computers in Human Behavior , 91 , 62–71. doi: 10.1016/j.chb.2018.09.025 [ CrossRef ] [ Google Scholar ]
• Pantic I, Damjanovic A, Todorovic J, Topalovic D, Bojovic-Jovic D, Ristic S, & Pantic S (2012). Association between online social networking and depression in high school students: Behavioral physiology viewpoint . Psychiatria Danubina , 24 ( 1 ), 90–93. [ PubMed ] [ Google Scholar ]
• Pew Research Center. (2018a). Teens, social media and technology 2018 . Retrieved from https://www.pewinternet.org/2018/05/31/teens-social-media-technology-2018/
• Pew Research Center. (2018b). Teens’ social media habits and experiences . Retrieved from https://www.pewinternet.org/2018/11/28/teens-social-media-habits-and-experiences/
• Przybylski AK, Murayama K, DeHaan CR, & Gladwell V (2013). Motivational, emotional, and behavioral correlates of fear of missing out . Computers in Human Behavior , 29 , 1841–1848. doi: 10.1016/j.chb.2013.02.014 [ CrossRef ] [ Google Scholar ]
• Radovic A, Gmelin T, Stein BD, & Miller E (2017). Depressed adolescents’ positive and negative use of social media . Journal of Adolescence , 55 , 5–15. doi: 10.1016/j.adolescence.2016.12.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reid Chassiakos YL, Radesky J, Christakis D, Moreno MA, Cross C; Council on Communications and Media. (2016). Children and adolescents and digital media . Pediatrics , 138 , e20162593. doi: 10.1542/peds.2016-2593 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Robinson JP, Kestnbaum M, Neustadtl A, & Alvarez AS (2002). The Internet and other uses of time In Wellman B & Haythornthwaite C (Eds.), The Internet in everyday life (pp. 244–262). Malden, MA: Blackwell. [ Google Scholar ]
• Rodriguez Puentes AP, & Parra AF (2014). Relación entre el tiempo de uso de las redes sociales en internet yla salud mental en adolescentes colombianos . Acta Colombiana de Psicología , 17 ( 1 ), 131–140. doi: 10.14718/ACP.2014.17.1.13 [ CrossRef ] [ Google Scholar ]
• Romer D, Bagdasarov Z, & More E (2013). Older versus newer media and the well-being of United States youth: Results from a national longitudinal panel . Journal of Adolescent Health , 52 , 613–619. doi: 10.1016/j.jadohealth.2012.11.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Salmela-Aro K, Upadyaya K, Hakkarainen K, Lonka K, & Alho K (2017). The dark side of internet use: Two longitudinal studies of excessive internet use, depressive symptoms, school burnout and engagement among Finnish early and late adolescents . Journal of Youth and Adolescence , 46 , 343–357. doi: 10.1007/s10964-016-0494-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sampasa-Kanyinga H, & Hamilton HA (2015). Social networking sites and mental health problems in adolescents: The mediating role of cyberbullying victimization . European Psychiatry , 30 , 1021–1027. doi: 10.1016/j.eurpsy.2015.09.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sampasa-Kanyinga H, & Lewis RF (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents . Cyberpsychology, Behavior, and Social Networking , 18 , 380–385. doi: 10.1089/cyber.2015.0055 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stack S (2003). Media coverage as a risk factor in suicide . Journal of Epidemiology & Community Health , 57 , 238–240. doi: 10.1136/jech.57.4.238 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steers MLN, Wickham RE, & Acitelli LK (2014). Seeing everyone else’s highlight reels: How Facebook usage is linked to depressive symptoms . Journal of Social and Clinical Psychology , 33 , 701–731. doi: 10.1521/jscp.2014.33.8.701 [ CrossRef ] [ Google Scholar ]
• Subrahmanyam K, & Greenfield P (2008). Online communication and adolescent relationships . The Future of Children , 18 ( 1 ), 119–146. doi: 10.1353/foc.0.0006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Szwedo DE, Mikami AY, & Allen JP (2011). Qualities of peer relations on social networking websites: Predictions from negative mother–teen interactions . Journal of Research on Adolescence , 21 , 595–607. doi: 10.1111/j.1532-7795.2010.00692.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tseng FY, & Yang HJ (2015). Internet use and web communication networks, sources of social support, and forms of suicidal and nonsuicidal self-injury among adolescents: Different patterns between genders . Suicide and Life-Threatening Behavior , 45 , 178–191. doi: 10.1111/sltb.12124 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tsitsika A, Janikian M, Schoenmakers TM, Tzavela EC, Ólafsson K, Wójcik S, … Richardson C (2014). Internet addictive behavior in adolescence: A cross-sectional study in seven European countries . Cyberpsychology, Behavior, and Social Networking , 17 , 528–535. doi: 10.1089/cyber.2013.0382 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tsitsika AK, Tzavela EC, Janikian M, Ólafsson K, Iordache A, Schoenmakers TM, … Richardson C (2014). Online social networking in adolescence: Patterns of use in six European countries and links with psychosocial functioning . Journal of Adolescent Health , 55 ( 1 ), 141–147. doi: 10.1016/j.jadohealth.2013.11.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Twenge JM, Joiner TE, Rogers ML, & Martin GN (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time . Clinical Psychological Science , 6 ( 1 ), 3–17. doi: 10.1177/2167702617723376 [ CrossRef ] [ Google Scholar ]
• Van Rooij AJ, Ferguson CJ, Van de Mheen D, & Schoenmakers TM (2017). Time to abandon Internet Addiction? Predicting problematic Internet, game, and social media use from psychosocial well-being and application use . Clinical Neuropsychiatry , 14 ( 1 ), 113–121. [ Google Scholar ]
• Wang P, Wang X, Wu Y, Xie X, Wang X, Zhao F, … Lei L (2018). Social networking sites addiction and adolescent depression: A moderated mediation model of rumination and self-esteem . Personality and Individual Differences , 127 , 162–167. doi: 10.1016/j.paid.2018.02.008 [ CrossRef ] [ Google Scholar ]
• Wang H, & Wellman B (2010). Social connectivity in America: Changes in adult friendship network size from 2002 to 2007 . American Behavioral Scientist , 53 , 1148–1169. doi: 10.1177/0002764209356247 [ CrossRef ] [ Google Scholar ]
• Woods HC, & Scott H (2016). #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem . Journal of Adolescence , 51 , 41–49. doi: 10.1016/j.adolescence.2016.05.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]

Sprinklr Service

Sprinklr Social

Works Best With

Sprinklr Insights

Sprinklr Marketing

Marketing Teams

Customer Service Teams

• Unified-CXM
• Customers Customer Stories Sprinklr Champions Sprinklr Community
• Company Our Story Leadership Newsroom Partners Careers Investor Relations Security & Data Privacy Sustainability
• Resources Learn Services Support Sprinklr Social ROI Estimator AI Maturity Assessment CX-WISE Analyst Reports Product Demo Days eBooks & Reports Events & Webinars Blog Unified-CXM Guide Forbes Influential CMO 2024 Our Services Training For Agencies Help Center Release Notes Contact Us

The undisputed leader in social media management

For over a decade, the world’s largest enterprises have trusted Sprinklr Social for its in-depth listening, unmatched channel coverage, enterprise-grade configurability and industry-defining AI.

Top Social Media Case Studies and Lessons to Learn

Social media case studies provide practical, actionable insights for your online marketing campaigns. They can highlight what works and what doesn’t. By learning from the experiences of others, you can refine your strategies to boost engagement and increase social media conversion rates for your business. 

As of 2024, the average internet user spends 143 minutes daily on social media. This high level of engagement presents a significant opportunity for brands to connect with potential customers. And adopting the best practices and strategies demonstrated in successful social media case studies can help you achieve this. 

In this blog, we’ll be exploring examples of social media case studies and their valuable lessons. So be sure to glean insights from them to ramp up your social media marketing game. 

Social media case studies of best social media brands

Using the right social media strategy can be a game changer for your brand as it will help you reach your audience effectively. Let’s look at a few success stories: 

#1: Spotify

In 2013, Spotify introduced its "Year in Review" feature. The company realized it had a treasure trove of streaming data. While the graphics were on-brand and less quirky than today’s versions, they still captured the audience’s attention.  

Fast-forward to 2016, Spotify rebranded these data stories as " Wrapped ." Each year, Spotify Wrapped introduces new and fun features based on users’ listening habits — from identifying your unique “audio aura” to categorizing you into one of 16 “listening personality types.”  

Why this campaign?    The campaign taps into users' love for content personalization and nostalgia, allowing them to reflect on their past year in music and share their unique listening habits with their friends and followers. This annual tradition has become a highly anticipated event. It generates buzz and drives customer retention and new sign-ups.  

What did they do?    Spotify collates vast amounts of listening data and presents it with eye-catching graphics that are instantly shareable on Facebook, Instagram and X. This enhances shareability and personalization, contributing to the viral success of Spotify Wrapped. 

How did it help?    Spotify Wrapped's social media metrics highlight its viral success. The campaign generated significant engagement, with more than 156 million users interacting with their personalized Wrapped summaries in 2022. What’s more, the first three days after its 2022 launch, Spotify Wrapped generated over 400 million tweets/X posts, highlighting its strong shareability and user engagement on social media platforms. 

What to take away?  

• Embed shareability for virality: Spotify Wrapped is highly shareable. The content is perfectly optimized for social media. The app even encourages you to share your Wrapped summary. Crafting personalized content that people are excited to share is a successful social media strategy , as active participation naturally follows when you engage your audience. 
• Create rituals: While Spotify Wrapped's format remains consistent, it gains more traction on social media each year. Annual campaigns, particularly those towards the year's end, establish a sense of ritual, and consumers are conditioned to anticipate them. Ritualized content gives your audience something to look forward to, year after year. 
• Jump on that bandwagon: Making brand-specific versions of popular content is a fantastic way to participate in the conversation. For instance, SEMRush used the Wrapped format to create unique content tailored to its audience. 

Deep Dive: How to adopt trends and broaden your channel coverage mix (while staying on brand)  

#2: The Barbie Movie 

The 2023 film Barbie brought the beloved Mattel doll to life in a dazzling, contemporary adventure. Starring Ryan Gosling and Margot Robbie, the movie seamlessly blends fantasy and reality as Barbie embarks on a transformative journey from her idyllic, pink-hued world to the real one. 

Why this campaign?  The Barbie marketing campaign was a masterclass in leveraging nostalgia, contemporary pop culture and innovative social media promotion strategies. It generated a massive buzz and captured the imagination of audiences worldwide. 

What did they do?    Warner Brothers and Mattel collaborated to create a multifaceted campaign. This included vibrant teasers, interactive social media content and high-profile brand partnerships. Here are the specifics:  

• Shared behind-the-scenes content, trailers and other promotional materials on all social media platforms 
• Engaged influencers and celebrities to create inspired memes and generate excitement 
• Created a sense of community by reflecting the diversity of its audience   
• Barbie-themed Malibu DreamHouse listed by Airbnb 
• Barbie-styled clothing line launched by Forever21 
• Barbie-themed meals offered by Burger King in Brazil 

• Utilized innovative tactics like a selfie generator to keep fans engaged 
• Benefitted from the unplanned "Barbenheimer" phenomenon 

The simultaneous release of Oppenheimer and Barbie really captured the public’s imagination. It sparked an unexpected cultural phenomenon known as "Barbenheimer." Christopher Nolan's intense biographical drama "Oppenheimer" explored the life of J. Robert Oppenheimer and the creation of the atomic bomb, while Greta Gerwig's "Barbie" was inspired by the iconic doll. The stark contrast between the two films captivated both audiences and the media.  

The phenomenon was fueled by social media buzz, memes and fans' enthusiasm for the unique cinematic experience of watching two drastically different films back-to-back. This boosted box office numbers for both movies and created a shared cultural moment transcending typical movie-going experiences. The playful rivalry and the combined marketing efforts led to unprecedented social media engagement , making "Barbenheimer" a standout event in 2023. 

How did it help?    The movie had grossed $1.45 billion worldwide , including $636 million in North America. Barbie was the top-grossing film of 2023, largely due to its strong social media engagement. In fact, in a Statista survey, more than half of the respondents claimed that they primarily learned about the movie through social media. 

• Generate buzz: The Barbie social media marketing campaign is a true masterclass in creating viral content. It clearly demonstrates that, when utilized effectively, social media can be an incredibly powerful tool for bringing a brand, product or film into the spotlight. Your marketing message becomes more powerful when it consistently appears in the feeds of your target audience .   

🤔 Are you looking to be in the public eye consistently?   

To be the talk of the “global town,” you need to be steady with your posting and ensure that your content is delivered at the ideal times. However, keeping track of all your social media posts and activities can often seem like a Herculean task. Though, there is a quick way to simplify all that manual heavy lifting.  

Sprinklr's Social Media Publishing & Engagement tool helps you streamline the organization, planning and execution of your social media content. With it, you can:

👁️ Get a complete overview of your posts, events and campaigns  

📚 Publish across 30+ channels at the same time 

🖼️ View comments, mentions and messages across channels in a single space 

🔎 Monitor the performance of your paid, owned and earned media in one single dashboard 

Pro Tip💡: Ensure your top content receives the exposure it merits. Influencers offer a distinctive viewpoint that authentically resonates with customers and potentially influences purchasing decisions. As such, using a tool like Sprinklr's AI-led Influencer Marketing Platform can help you find the right influencers across multiple demographics. It identifies the best-suited influencers to maximize the reach, authenticity and impact of your marketing message, and it also gives deeper insights into their performance. 

• Innovate to resonate: Barbie's marketing campaign showcased various inventive strategies to captivate fans. Due to the interactive experiences offered through the film's promotion, audiences remained eagerly anticipating its release. For instance, typing "Barbie," "Margot Robbie," "Ryan Gosling" or "Greta Gerwig" into Google search triggered a magical makeover and turned the screen pink with sparkles, thrilling fans worldwide. 
• Be inclusive: The Barbie campaign embraced inclusivity by appealing to various audiences. By featuring diverse characters and themes in its marketing materials, Barbie made audiences feel represented and included in the brand's narrative. The propagation of social media accessibility and inclusivity strengthened Barbie's connection with its fan base and drew new audiences.

Related Read: 10+ Ideas for Social Media Posts That Move the Needle  

#3: Shiseido Japan 

Shiseido Japan is a renowned global beauty and cosmetics company with a rich heritage of combining Eastern aesthetics and Western science. The company has established itself as a leader in the beauty industry and is known for its innovative skincare, makeup and fragrance products.  

Why this campaign?    Historically, Shiseido Japan's makeup marketing teams collaborated with agencies to monitor social media performance. They relied on agency-provided reports or manually checked each social account, which prevented them from responding quickly to any issues. In 2021, Shiseido Japan decided to overhaul the marketing strategies for makeup brands like MAQuillAGE, Snow Beauty, INTEGRATE and MAJOLICA MAJORCA. 

What did they do?    Shiseido adopted Sprinklr's Unified-CXM platform to transform its marketing teams. It helped them collaborate effortlessly by combining tools and data on a single platform.

Pro Tip💡: Automate and manage workflows , such as campaign deployment and reporting, to free up time for teams to focus on strategic initiatives. Look for platforms or software solutions that offer features such as campaign scheduling, account addition via emails and customizable dashboards. Modern platforms like Sprinklr Social and Sprinklr Insights are purpose-built for this task, with all the aforementioned offerings and more. They unify channels, tools and data, providing a comprehensive view of the customer.

How did it help?    Sprinklr significantly enhanced Shiseido's social media management by centralizing all media accounts on a single platform. It allowed the marketing teams to access real-time data through customized dashboards and generate automated, shareable reports, enabling better social media measurement. This resulted in a 244% increase in overall owned media account performance in 2022.  

The shift to Sprinklr also facilitated a new data-driven culture for social campaigns, moving away from guesswork. Teams could validate ideas, check for viral potential using past trends and analyze campaign performance against social media KPIs (key performance indicators), making the necessary adjustments for future campaigns. This structured approach resulted in better campaign outcomes. Mentions of Shiseido's makeup brands on social media through user-generated content (UGC) increased by 406% in 2022, when compared to 2021. 

• Facilitate data-driven decision-making: Like Shiseido, consider investing in technologies like Sprinklr's Unified-CXM platform . It will transform your decision-making processes by providing real-time actionable insights by analyzing data collected across multiple channels.  
• Prioritize social listening : Make sure you prioritize social listening to analyze audience sentiment, track trends and identify opportunities for brand engagement. By actively monitoring social media conversations, engaging with your audience and leveraging UGC, you can foster authentic connections with your customers. 
• Customize your dashboards: Tailor your social media analytics tools to your unique needs, just like Shiseido did with Sprinklr's dashboards. By customizing metrics and dashboards to track social media KPIs relevant to your business goals, you'll gain deeper insights into your social performance. 

Read More : Sprinklr’s Social Media Case Study on Shiseido Japan

How do you write a social media growth case study?  

Now that we’ve examined some great social media case studies, let's explore how you can create one. Here’s how you can structure and populate one on your own:  

1. Introduction 

• Introduce the brand: Start by briefly introducing the brand whose social media growth you are examining. 
• State the purpose: Explain the purpose of the case study and what you aim to demonstrate. 

2. Background

• Ground zero: Describe the initial state of the brand's social media presence. Include metrics like follower count, engagement rates and notable challenges. 
• Objectives: Clearly outline the brand's goals for social media growth. These could be increasing followers, boosting engagement or enhancing brand awareness . 

3. Strategy 

• Target audience: Define the target audience for social media efforts. 
• Platform selection: Explain which social media platforms were chosen and why. 
• Content strategy: Explain what types of content were created and their messaging. 
• Campaigns: Describe the brand’s advertising campaigns, apart from influencer partnerships, paid advertising or user-generated content campaigns. 

4. Implementation 

• Timeline: Provide a timeline of key activities and milestones. 
• Resources: Discuss the resources used, including team members, tools and budget. 

5. Metrics 

• Growth metrics: Present metrics that indicate growth, such as changes in follower count, engagement rates, reach and impressions . 
• Additional metrics: Include other relevant metrics like click-through rates, conversion rates and social media ROI . 

6. Results 

• Compare and contrast: Compare the initial metrics with the final results to show the growth. 
• Achievements: Highlight major achievements and milestones reached during the campaign. 

7. Solutions 

• Obstacles: Discuss any challenges faced during the campaign. 
• Fixes: Explain how these challenges were addressed and what solutions were implemented. 

8. Takeaways 

• Successful strategies: Summarize the social media growth strategies that worked well and contributed to success. 
• Lessons learned: Reflect on what could have been done differently and any lessons learned for future campaigns. 

9. Conclusion 

• Summary: Recap the main points and results of the case study. 
• The future: Briefly mention any next steps for the brand’s social media strategy. 

10. Visual elements  

• Charts and graphs: Use reporting charts and graphs to visually represent the data and growth metrics. 
• Screenshots: Include screenshots of successful posts, campaign highlights and other visual elements.  

Final thoughts  

These social media case studies showcase innovative strategies for capturing audience attention and driving significant engagement. However, this is easier said than done. The challenge for large brands in social media marketing lies in effectively leveraging data insights and managing multiple channels. 

Sprinklr Social can help you with this. It offers real-time actionable data insights, streamlines social media management and enables personalized engagement with audiences. The platform is trusted by global companies for its in-depth listening, unmatched channel coverage of 30+ digital channels and enterprise-grade configurability. This is so you’re always in the know of every customer interaction. The best part? It automates your end-to-end social media management with the industry-leading Sprinklr AI and accelerates content creation with top-tier generative AI capabilities. 

Keen to find out how this software can elevate your social media marketing efforts?  

Frequently Asked Questions

Get the social management tool that does it all

Easily create and schedule posts, publish at the best times, and maximize your social engagement across 10+ channels.