social media research studies

Advances in Social Media Research: Past, Present and Future

• Open access
• Published: 06 November 2017
• Volume 20 , pages 531–558, ( 2018 )

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• Kawaljeet Kaur Kapoor 1 ,
• Kuttimani Tamilmani 2 ,
• Nripendra P. Rana 2 ,
• Pushp Patil 2 ,
• Yogesh K. Dwivedi 2 &
• Sridhar Nerur 3  

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Social media comprises communication websites that facilitate relationship forming between users from diverse backgrounds, resulting in a rich social structure. User generated content encourages inquiry and decision-making. Given the relevance of social media to various stakeholders, it has received significant attention from researchers of various fields, including information systems. There exists no comprehensive review that integrates and synthesises the findings of literature on social media. This study discusses the findings of 132 papers (in selected IS journals) on social media and social networking published between 1997 and 2017. Most papers reviewed here examine the behavioural side of social media, investigate the aspect of reviews and recommendations, and study its integration for organizational purposes. Furthermore, many studies have investigated the viability of online communities/social media as a marketing medium, while others have explored various aspects of social media, including the risks associated with its use, the value that it creates, and the negative stigma attached to it within workplaces. The use of social media for information sharing during critical events as well as for seeking and/or rendering help has also been investigated in prior research. Other contexts include political and public administration, and the comparison between traditional and social media. Overall, our study identifies multiple emergent themes in the existing corpus, thereby furthering our understanding of advances in social media research. The integrated view of the extant literature that our study presents can help avoid duplication by future researchers, whilst offering fruitful lines of enquiry to help shape research for this emerging field.

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1 Introduction

Social media allows relationship forming between users from distinct backgrounds, resulting in a tenacious social structure. A prominent output of this structure is the generation of massive amounts of information, offering users exceptional service value proposition. However, a drawback of such information overload is sometimes evident in users’ inability to find credible information of use to them at the time of need. Social media sites are already so deeply embedded in our daily lives that people rely on them for every need, ranging from daily news and updates on critical events to entertainment, connecting with family and friends, reviews and recommendations on products/services and places, fulfilment of emotional needs, workplace management, and keeping up with the latest in hashion, to name but a few.

When we refer to social media, applications such as Facebook, WhatsApp, Twitter, YouTube, LinkedIn, Pinterest, and Instagram often come to mind. These applications are driven by user-generated content, and are highly influential in a myriad of settings, from purchasing/selling behaviours, entrepreneurship, political issues, to venture capitalism (Greenwood and Gopal 2015 ). As of April 2017, Facebook enjoys the exalted position of being the market leader of the social media world, with 1.97 billion monthly users (Statista 2017 ). In addition to posts, social media sites are bombarded with photo and video uploads, and according to the recent numbers, about 400 million snaps a day have been recorded on Snapchat, with around 9000 photos being shared every second (Lister 2017 ). While 50 million businesses are active on Facebook business pages, two million businesses are using Facebook advertising. Apparently, 88% businesses use Twitter for marketing purposes (Lister 2017 ).

Academics and practitioners have explored and examined the many sides of social media over the past years. Organizations engage in social media mostly with the aim of obtaining feedback from stakeholders (Phang et al. 2015 ). Consumer reviews are another big part of social media, bringing issues of information quality, credibility, and authenticity to the forefront. To a large extent, online communities have been successful in bringing together people with similar interests and goals, making the concept of micro blogging very popular. While most messages exchanged on social media sites are personal statuses or updates on current affairs, some posts are support seeking, where people are looking for assistance and help. Interestingly, these have been recognized as socially exhausting posts that engender social overload, causing other members to experience negative behavioural and psychological consequences, because they feel compelled to respond (Maier et al. 2015a ).

Given the relevance of social media to various stakeholders, and the numerous consequences associated with its use, social media has attracted the attention of researchers from various fields, including information systems. This is evidenced by the large number of scholarly articles that have appeared in various outlets. Researchers have to expend an enormous amount of time and effort in collating, analysing, and synthesising findings from existing works before they embark on a new research project. Given the significant number of studies that have already been published, a comprehensive and systematic review can offer valuable assistance to researchers intending to engage in social medi research. Our literature search suggests that there are reviews on social media in the marketing context (see for example, AlAlwan et al. 2017 ; Dwivedi et al. 2017a ; Dwivedi et al. 2015 ; Ismagilova et al. 2017 ; Kapoor et al. 2016 ; Plume et al. 2016 ). However, there exists no comprehensive review that integrates and synthesises the findings from the articles published in Information Systems journals. Such an endeavour will not only provide a holistic view of the extant research on social media, but will also provide researchers a comprehensive intellectual platform that can be used to pursue fruitful lines of enquiry to help advance research in this rapidly expanding area. To fulfill this goal, this study reviewed relevant articles to elucidate the key thematic areas of research on social media, including its benefits and spill-over effects. The resulting review is expected to serve as a one-stop source, offering insight into what has been accomplished so far in terms of research on social media, what is currently being done, and what challenges and opportunities lie ahead. By doing so, this study explores the following aspects of existing research on social media:

How is social media defined in the IS literature?

How has social media literature evolved from a multidisciplinary perspective?

How have social media technologies, applications, practices, and research evolved over the past 20 years?

Which social media issues and themes have already been examined in IS research?

What are the major limitations of extant literature on social media?

The next section of this paper gives a brief overview of the method employed for carrying out the literature search. The succeeding section discusses citation and text analyses of social media publications. Subsequently, we outline the various ways in which scholars have defined social media. This is followed by a section that focuses on the evolution of social media research from an IS perspective. Next, we articulate the major themes emerging from prior research and use them as a backdrop for our review of the literature on social media. The ensuing section discusses our findings, followed by key conclusions and limitations of the study.

2 Literature Search Method

The literature search for this analysis was conducted in the following two phases: (1) keyword-based search and analysis to explore the overall evolution of social media literature; and (2) manual search across specific IS journals to understand the emerging IS perspectives on this topic.

2.1 Keywords Based Search and Analysis

In order to gain a deeper understanding of social media, we analyzed relevant abstracts that were downloaded from the Web of Science (WOS) database. Our search terms Footnote 1 yielded a total of 13,177 records, out of which 12,597 unique abstracts were obtained. The analysis of these records was undertaken in two steps. First, we used VOSviewer (Van Eck and Waltman 2011 ) to perform a co-citation analysis of first authors in the downloaded corpus. VOSviewer allows visualization of similarities in publications and authors through an examination of bibliometric networks. Furthermore, we used VOSviewer to analyze words derived from titles and abstracts. Second, we used Latent Dirichlet Allocation (LDA) (see Blei 2012 ) to extract key thematic areas latent in the literature on social media. Further details about these analyses and results are presented in section 3 .

2.2 Manual Search and Analysis

Given the inconsistencies in the use of keywords in social media research, a manual search, rather than a keyword-based one, was deemed to be more appropriate for identifying the existing literature on social media. Furthermore, since keywords in the social media literature tend to overlap with topics and/or theories in other related research areas, a keyword search may yield irrelevant articles. For instance, a keyword search for “Social network” returns articles related to social network theories, which are not necessarily part of social media. The articles reviewed in this study are from the following eight Senior Scholars’ Basket of Information Systems journals: European Journal of Information Systems (EJIS); Information Systems Journal (ISJ); Information Systems Research (ISR); Journal of the Association for Information Systems (JAIS); Journal of Information Technology (JIT); Journal of Management Information Systems (JMIS); Journal of Strategic Information Systems (JSIS) and Management Information Systems Quarterly (MISQ)). Along with these eight journals, we have also analysed relevant articles from Information Systems Frontier (ISF) journal. This is because it focuses on examining “new research and development at the interface of information systems (IS) and information technology (IT) from analytical, behavioural, and technological perspectives. It provides a common forum for both frontline industrial developments as well as pioneering academic research”. Footnote 2 ISF enjoys the reputation of a high quality journal across continents. For example, a journal quality ranking by Chartered Association of Business Schools, UK, has given it a three star (high ranking) quality rating, while journal ranking by the Australian Business Deans Council (ABDC) has rated it as an ‘A’ class journal (the second highest quality journal category after A*, which is reserved for premier publications). In light of these observations, it was deemed appropriate to consider articles from ISF along with the aforementioned eight journals.

Relevant articles were then identified and downloaded from each of the target journals by going through their archives. Specifically, all volumes and issues published in these journals between 1997 and 2017 were considered in our analysis. Articles, research notes, introductions, research commentaries, and editorial overviews relevant to social media were downloaded and numbered to prepare an APA style reference list. The first literature search resulted in 181 articles that had some relevance to the social media domain. A closer examination of individual abstracts and full articles led to the elimination of 49 irrelevant articles, thus giving us a total of 132 articles pertinent to the domain of interest (i.e., social media).

3 Citation and Text Analyses of Social Media Publications

3.1 author co-citation analysis (aca).

Author Co-Citation Analysis (ACA) is a bibliometric technique that has been widely used to explicate the conceptual structure of disciplines (for example, see White and Griffith 1981 ; McCain 1984 ; Culnan 1986 ; Nerur et al. 2008 ). The underlying assumption in ACA is that authors who are frequently cited together tend to work on similar concepts. Thus, frequently co-cited authors are likely to cluster together when an ACA is performed. VOSviewer considers only first authors when it performs ACA. Only authors who had 50 or more citations were included in the analysis. Figure  1 shows the results of ACA.

Author clusters from ACA

VOSviewer identified seven distinct clusters:

Cluster 1: Authors in this cluster have contributed to research on Twitter (e.g., Sakaki), social network analysis (e.g., Wasserman), topic modeling (e.g., Blei), sociality and cognition (e.g., Dunbar), sentiment analysis of tweets (e.g., Thelwall), and other related topics.

Cluster 2: Authors in this cluster are well known for their work on technology adoption (e.g., Venkatesh), diffusion of technology (Rogers), culture (Hofstede), theory of planned behavior (Ajzen), marketing/consumer behavior (e.g., Hennig-Thurau), and statistical methods (e.g., Bagozzi, Fornell, Hair).

Cluster 3: This cluster comprises of authors who deal with a variety of issues related to social media (Facebook and Twitter) use. For example, Steinfied and Ellison examined social capital across Facebook; Kuss studied online/social networking addiction (e.g., gaming addiction), and Lenhart focused on teens and technology (e.g., mobile internet use), particularly in the use of social media. Other topics include Bandura’s self-efficacy, use and benefits of Twitter by scholars, and personality and social characteristics of Facebook users (e.g., Ross).

Cluster 4: Prominent social theorists/sociologists who have contributed to social capital theory, structuration theory and modern sociological theory are distinguished members of this cluster. These include Bourdieu, Coleman, Giddens, and Habermas. Papacharissi has written about a variety of topics including the exploration of factors that predict Internet use as well as users’ behaviors, identity, sense of community and culture on social media. Tufekci has studied privacy and disclosure on social media, as well as other topics, including how social networking sites such as Facebook might influence one’s decision to participate in protests.

Cluster 5: In this cluster, there is evidence of the influence of Vygotsky’s socio cultural learning theory as well as Lave and Wenger’s work on communities of practice. In addition to his work on collaborative learning, Kirschner has examined the relationship between Facebook and academic performance. Likewise, Selwyn has explored pedagogical and learning engendered by the use of information and computer technologies (ICT).

Cluster 6: This cluster appears to reflect two broad themes. The first is a range of topics related to medical Internet research, broadly referred to as e-health (Eysenbach) or online health (Duggan). Themes in this category include electronic support groups and health in virtual communities (Eysenbach), and policies and healthcare associated with social media, and professionals among medical students and physicians in the use of social media (Chretien, Greysen). The second main thematic area in this cluster deals with scholarship on social media, scholarly communication, and metrics for evaluating impact of articles on the web (e.g., Weller, Bormann, Priem).

Cluster 7: The dominant theme here is the nature and content of communication. In particular, scholars in this cluster have focused on communication and response in the face of crises (Coombs), including image restoration after a controversy (Benoit), analysis and reliability of content (Krippendorff), and the use of social media sites such as Facebook and Twitter by government agencies and non-profit organizations to engage stakeholders (Waters).

3.2 Text Analysis of Words in Titles and Abstracts

VOSviewer was used to analyze terms (i.e., words) in the titles and abstracts of our corpus to obtain a two-dimensional map showing proximities of words that are likely to be related based on their co-occurrences. Specifically, VOSviewer relies on the Apache OpenNLP Toolkit to identify noun phrases, and then compares their overall co-occurrence distribution with their distribution across other noun phrases to compute a relevance score (Van Eck and Waltman 2011 ). The intuition is that frequently co-occurring noun phrases with high relevance are likely to unravel a topic or theme that is latent in the corpus. The term map from VOSviewer is shown in Fig.  2 . Only terms that occurred 50 times or more were included. Furthermore, relevance scores computed by VOSviewer for every term were used to select the top 80% that met the threshold.

Term map showing clusters of related words/noun phrases

VOSviewer identified five clusters here. It is evident from the clusters that research on social media has dealt with a broad range of topics, including but not restricted to diffusion of information and opinions, spread of diseases (e.g., influenza), identification of social and emotional health concerns and attendant interventions to deal with them, social media as an influence, the use of social media for marketing purposes, and the implications of social media as a tool for pedagogy (i.e., teaching and learning) and medical practice. These have been summarized in Table  1 .

It must be noted that the topics are broad and don’t reveal the nuances of research areas embodied in the abstracts examined in this study. The next sub-section presents the results of topic modeling, which has the potential to unravel more focused themes embodied in the large corpus that we analyzed.

3.3 Topic Modeling

The fact that our search terms yielded over 12,000 abstracts suggests that scholars are investing increased interest on research issues related to social media. While an informed researcher may have a general idea of the nature of research undertaken so far, it is humanly impossible to discern the thematic structure of all scholarly documents available on social media. Recent advances in topic modeling have made this task relatively easy. Topic modeling relies on algorithms and statistical methods to elicit the topics latent in a large corpus (Blei 2012 ). The term topic refers to a specific and often recognizable theme defined by a cohesive set of words that have a high probability of belonging to that topic. There are several options available for topic modeling: non-negative matrix factorization (NNMF), Latent Semantic Analysis/Indexing (LSA/LSI), and Latent Dirichlet Allocation (LDA). In this study, we use LDA, arguably the most widely used topic modeling algorithm. In order to perform topic modeling on a corpus, the researcher has to specify the number of topics to be extracted. In this study, we extracted the top 100 topics reflected in the scholarship on social media. LDA starts with the assumption that each abstract in our study reflects each of these topics to varying degrees (Blei 2012 ). Thus, each abstract has a distribution of the desired 100 topics. The 100 topics that were extracted from our abstracts are shown in Table  2 . The machine learning for language toolkit (MALLET) (McCallum 2002 ) was used for this purpose.

4 Analysis of Social Media Research from an IS Perspective

4.1 how is social media defined in the is literature.

In studying the existing literature on social media, it becomes apparent that the authors in this field have not focussed on defining social media. Of all the studies included in this review, only a handful of studies have come close to defining, or clarifying the concept of social media. For instance, Lundmark et al. ( 2016 , p3) suggest, “social media, as a unique form of communication, integrates multiple sources of legitimacy, and as a result, presents a unique and important context through which to study the topic. Indeed, social media are a means for the dissemination of both internally and externally generated information pertaining to firms, industries, and society in general.” According to Schlagwein and Hu ( 2016 ), social media constitutes internet-based communication and collaboration channels, widely in use since 2005, and, from an IS perspective, social media tools and their surrounding organizational and managerial structures constitute social information systems. Wakefield and Wakefield ( 2016 , p140) describe “social media technologies as an ensemble IS artefact composed of technical, informational, and relational subsystems that interact distinctly according to the context of use.” In their study, they also identify a “recent definition of social media and social networks referring to social media networks as specific types of social media platforms and Internet sites with common attributes such as (1) user profile (2) user access to digital content (3) a user list of relational ties, and (4) user ability to view and traverse relational ties” (Wakefield and Wakefield 2016 ; p144).

In a more relatable and simple definition, Miranda et al. ( 2016 ; p304) explain social media being “mainly conceived of as a medium wherein ordinary people in ordinary social networks (as opposed to professional journalists) can create user-generated news.” A few other authors like Spagnoletti et al. ( 2015 ) and Xu and Zhang ( 2013 ) commonly refer to social media as a set of interned-based technologies/applications, which are aimed at promoting the creation, modification, update and exchange of user-generated content, whilst establishing new links between the content creators themselves. Bharati et al. ( 2014 ; p258) refer to social media as a technology “not focussed on transactions but on collaboration and communication across groups both inside and outside the firm.” Lastly, Tang et al. ( 2012 ; p44) also identify social media as user-generated media, which is a source of “online information created, initiated, circulated, and used by consumers intent on educating each other about products, brands, services, personalities, and issues.”

All of the aforementioned descriptions clearly regard social media as communication tools supported by internet-based technologies for dissemination of information. Most of them acknowledge the high concentration of user generated content across such platforms. Based on our understanding of social media and the aforementioned definitions, we propose the following definition: Social media is made up of various user-driven platforms that facilitate diffusion of compelling content, dialogue creation, and communication to a broader audience. It is essentially a digital space created by the people and for the people, and provides an environment that is conducive for interactions and networking to occur at different levels (for instance, personal, professional, business, marketing, political,and societal) .

4.2 Evolution of Social Media Research in the IS Literature

In the past two decades, various issues related to social media have been examined in line with the rapid evolution of underlying technologies/applications and their appropriation to enable different types of social media usage. An analysis of 132 articles from selected IS journals suggests that publications until 2011 were still examining user-generated content as a new type of online content (Burgess et al. 2011 ). However, in the last six years, research in this field has made tremendous progress, not just in terms of its scope, but also in explicating the highs and lows associated with the use of social media. While it is difficult to pinpoint evolution on a yearly basis, it has been possible to identify the major aspects of social media research that have emerged over time. Publications between 1997 and 2017 have been reviewed here. Interestingly, only one publication of interest to this study (Griffiths and Light 2008 ) was identified between the period 1997 and 2009.

Out of the 132 studies individually reviewed here, about 21 studies examined the behavioural side of social media use. While most of the initial studies (for instance, Massari 2010 ; Garg et al. 2011 ) restricted interest to peer influence and information disclosure willingness (2010–2012), the latter studies (for instance, Gu et al. 2014 ; Krasnova et al. 2015 ) were seen to be more exploratory in examining the positive, dysfunctional, cognitive and affective, heterophily and homophily tendencies of social media users (2012–2016). There were 18 studies investigating the very popular aspect of reviews and recommendations on social networks, with 2013 being a popular year for such studies. Most of these studies (for instance, Hildebrand et al. 2013 ; Zhang and Piramuthu 2016 ) were interested in improving their understanding of the information quality of these reviews and the associated consequences (2010–2016). There were 17 studies (2011–2016) evaluating the integration of social media for varied organizational purposes . While some studies investigated the employee side (e.g., innovativeness, retention, and motivation) of social media use (for instance, Aggarwal et al. 2012 ; Miller and Tucker 2013 ), the others discussed the relationship between social enterprise systems and organizational networking (for instance, Trier and Richter 2015 ; Van Osch and Steinfield 2016 ).

Around 13 publications studied the use of social media as a marketing tool . The early studies here (2010–2013) explored consumer purchase behaviour and firm tactics, such as involving consumers in marketing strategies (for instance, García-Crespo et al. 2010 ; Goh et al. 2013 ). The later studies (2015–2016), however, became more focussed on studying social commerce across networking sites such as Facebook, MySpace, and YouTube (e.g., Chen et al. 2015 ; Sung et al. 2016 ). Ten studies were interested in online communities and blogging (see Singh et al. 2014 ; Dennis et al. 2016 ). These were mostly interested in blogger behaviours, reader retention, online content, contributing capacity, and blog visibility (2011–2016). Nine publications revealed the risks associated with the use of social media. These are either very early studies (2008–2010; for instance, Tow et al. 2010 ) or fairly recent (2014–2016) learning about scamming and farcing issues faced by users. They focus on combating issues of privacy and security, whilst trying to differentiate between fake and authentic online content (for instance, Zhang et al. 2016 ).

Up until 2015, about eight studies analysed the negative stigma attached to using social media at the workplace (for instance, Koch et al. 2013 ). While a couple of studies also revealed the positive side of social media (for instance, Lu et al. 2015 ), most were seen discussing its ill-effects on work outputs, routine performance, and clash of notions in the personal and professional space (for instance, Ali-Hassan et al. 2015 ). About seven studies were interested in exploring the relationship between social media use and value creation (for instance, Luo et al. 2013 ; Barrett et al. 2016 ) in terms of firm equity, customer retention, social position, and firm value (2010–2016). Another seven studies investigated the use of media sites to share and exchange information during natural disasters and critical events (2011–2015). Interestingly, most of the studies documenting this aspect of social media used Twitter data for their analyses (for instance, Oh et al. 2013 ; Lee et al. 2015a ). A very small percentage of studies (five studies) in 2014 and 2015 focussed on analysing the effects of social media posts that were seeking help/support from other social media users (for instance, Spagnoletti et al. 2015 ; Yan et al. 2015a ). Only a handful of studies (five studies), particularly in 2010 and 2016, were examined the use of social media in public administration and political contexts, such as open governance and transparency (for instance, Baur 2017 ; Rosenberger et al. 2017 ). Also, just about three studies (Wattal et al. 2010 ; Dewan and Ramaprasad 2014 ; Miranda et al. 2016 ) dedicated their efforts to comparing traditional media with social media . The last set of studies (2013–2016), around nine in total (for instance, Bharati et al. 2014 ; Chung et al. 2017 ), were identified as those limiting themselves to developing and testing social media constructs in relation to previously established theories and models (technology acceptance model, theory of planned behaviour, and others).

4.3 Literature Synthesis

As outlined in the previous section, social media research is evolving at a fast pace. In reviewing the shortlisted articles, various themes were identified based on the similarities observed across the issues addressed in social media research.

4.3.1 Social Media Use Behaviours and Consequences

Many scholars explore the behavioural side of social media, and interestingly, some find factors that prevent users from continuing its use. Turel and Serenko ( 2012 ) warn against excessive use of social media sites, which can result in strong pathological and maladaptive psychological dependency on social media. In a subsequent study, Turel ( 2015 ) used cognitive theory to reveal that guilt feelings associated with the use of a website can increase discontinuance intentions. Matook et al. ( 2015b ) show that online social networks can be linked with perceived loneliness, which depends on user’s active/passive engagement with social media. Krasnova et al. ( 2015 ) suggest that in response to social information consumption, envy plays a significant role in reducing cognitive and affective wellbeing of a user. However, Maier et al. ( 2015b ) disclose that, while social networking stress creators can increase discontinuance intentions, switching stress-creators and exhaustion (i.e. switching to alternatives) can reduce such intentions. Chang et al. ( 2014 ) find that dissatisfaction and regret, alternative attractiveness, and switching costs affect switching intentions. Xu et al. ( 2014 ) find that dissatisfaction from support and entertainment values, continuity cost and peer influence encourage switching between social networks.

Wakefield and Wakefield ( 2016 ) focus on Facebook and Twitter to show that excitement combined with passion acts as a favourable factor for increased social media engagement. Chiu and Huang ( 2015 ) use media communication theories to show that user gratification from social networking sites positively affects their social media usage intention. In studying virtual investment communities, Gu et al. ( 2014 ) reveal that despite benefits of heterophily, investors are allured by homophily in their interactions. Zeng and Wei ( 2013 ) analyse Flickr data and find that at the time of forming a social tie, members exhibit similar behaviour, which evolves differently later. Shi et al. ( 2014 ) examine retweet relationships and find that those with weak ties have a higher probability of engaging in content sharing. Kreps ( 2010 ) introduces poststructuralist critique to explore how closely an individual’s personality is reflected in their social media profile, such as Facebook.

Chen et al. ( 2014 ) find affective and continuance types of commitments to be good predictors of user behaviours on social media sites. Stieglitz and Dang-Xuan ( 2013 ) examine the relationship between user behaviour and sentiment to conclude that emotional Twitter messages have a higher retweet tendency. Khan and Jarvenpaa ( 2010 ) analyse event creation pages on Facebook to find that the social groups demonstrate differential interactive behaviour prior and post the midpoint of event creation. Chen and Sharma ( 2015 ) disclose that the extent of self-disclosure on social media sites depends on member attitude. Massari ( 2010 ) finds that MySpace users tend to disclose substantial personal details that put them at the risk of security and privacy breach. Xu et al. ( 2016 ) find that one’s image and moral beliefs combined with community policies and peer pressure act as deterrents to aggression on social media. Garg et al. ( 2011 ) measure peer influence in an online music community and find that peers can significantly increase music discovery. Susarla et al. ( 2012 ) examine video and user information dataset from YouTube, and find that the success of a video hugely depends on social interactions, which also determines its impact magnitude.

The review of studies related to this theme suggests that since 2010, IS researchers have focussed on examining the dysfunctional consequences of social media adoption, such as - addiction, stress, information overload, and others. Use behaviour was examined across a variety of platforms like Facebook, Twitter, MySpace, and Flickr. Media content, such as picture, video, and tweets have also been explored by the studies in this category.

4.3.2 Reviews and Recommendations on Social Media Sites

A predominant characteristic of social media networks is product/service reviews and recommendations. People are beginning to rely on others’ experiences, for instance, before making a purchase, visiting a place, or searching for accommodation.. Such online reviews complement product/service information. An early study on online travel information found that consumers invest higher trust in reviews published on government/tourism websites in comparison to those on a social media site (Burgess et al. 2011 ). Hwang et al. ( 2011 ) analysed the social bookmarking sites for impact of positive and negative reviews on collective wisdom and found that negative reviews are capable of stabilizing system performance. Dellarocas et al. ( 2010 ) suggest that online forums looking to increase reviews of lesser-known products should make information on previously posted reviews a less prominent feature. Cheung et al. ( 2012 ) empirically tested a consumer review website to conclude that argument quality, review consistency, and source are critical for assessing review credibility.

Chen et al. ( 2011 ) investigate the effect of moderation and reveal that the commentators generate high quality content to build a stronger reputation. Wei et al. ( 2013 ) developed a multi-collaborative filtering trust network algorithm for Web 2.0 with improved accuracy for filtering information based on user preferences and trusted peer users. Luo and Zhang ( 2013 ) refer to user-generated reviews and recommendations as consumer buzz to find that advocacy and consumer attitude can impact firm value. Hildebrand et al. ( 2013 ) use data from a European car manufacturer allowing self-designed products to reveal that feedback from other community members lessens uniqueness whilst increasing dissatisfaction. Centeno et al. ( 2015 ) address the skewed reputation rankings problem in movie ratings by suggesting the use of comparative user opinions. Ma et al. ( 2013 ) analyse data from Yelp to test bias in online reviews and find that frequent and longer reviews successfully combat such biases. Lukyanenko et al. ( 2014 ) demonstrate that participants tend to provide accurate information in classifying a phenomenon at a general level, and higher accuracy where they are allowed free form data. Shi and Whinston ( 2013 ) explore the possible impact of friend check-ins on social media, and find it has no positive effect in generating new user visits.

Goes et al. ( 2014 ) disclose that user popularity results in increased and objective reviews, while numeric ratings turn more varied and negative with it. Matook et al. ( 2015a ) use relationship theories to show that past recommendation experience, closeness, and excessive posting behaviour positively affect trust and person’s intention to act on the made recommendation. Yan et al. ( 2015b ) evaluate revisit intentions for restaurants, and find that food and service quality, price and value, and the atmosphere govern such intentions. Kuan et al. ( 2015 ) analysed Amazon reviews and observed that certain characteristics such as length, readability, valence, extremity, and reviewer credibility are more likely to be recognized. In a different study, Zhang and Piramuthu ( 2016 ) suggest that product/service information on seller’s websites are often limited, and propose a Latent Dirichlet Allocation model to reveal the useful complementary hidden information in customer reviews. In a parallel conversation, Wu and Gaytán 2013 suggest that buyers integrate product price with seller reviews in configuring their willingness to pay.

The review under this theme suggests that studies as early as 2010 focussed on evaluating the authenticity of product and service reviews/recommendations published online. Overall, these studies reveal that the effect of review volume is often moderated by a buyer’s risk attitude. Most studies identify that the combination of consumer’s interest and available reviews helps users choose products/services that offer best value to them.

4.3.3 Social Media and Associated Organizational Impact

Publications have also shown interest in investigating the effects of user-generated content on entrepreneurial behaviour. For instance, Greenwood and Gopal ( 2015 ) find that discourse in both traditional and user-generated media has a notable influence on IT firm founding rates. Lundmark et al. ( 2016 ) reveal that higher usage of Twitter, alongside follower numbers and retweets result in higher levels of under pricing for initial public offerings (IPO). Trier and Richter ( 2015 ) find that online organizational networking has many unbalanced multiplex relationships, mostly comprising of weak ties and temporal change. They attribute the uneven user contribution in social networking sites to discourse drivers and information retrievers. Schlagwein and Hu ( 2016 ) identify collaboration, broadcast, dialogue, sociability, and knowledge management as the social media types that serve varied organizational purposes. Claussen et al. ( 2013 ) study Facebook to conclude that social media networks can exercise management not only by excluding participants, but also by driving softer changes in incentive/reward systems.

Subramaniam and Nandhakumar ( 2013 ) study enterprise system users and find that integrating social media facilitates user interaction that helps embed relationship ties between virtual actors. Another study concerning social features in enterprise systems reveals that business interactions are less social, and highly context specific (Mettler and Winter 2016 ). Van Osch and Steinfield ( 2016 ) showed that the enterprise system user involved in social network posting will show differences in team boundary spanning activities based on their hierarchical position (leadership, team member, etc.). Benthaus et al. ( 2016 ) analyse Twitter data to find that social media management tools have a catalysing effect on employee output as they enrich the user engagement process. Gray et al. ( 2011 ) study the social bookmarking system to find that social diversity of information sources is a good predictor of employee innovativeness. Kuegler et al. ( 2015 ) show that using enterprise social networking within teams strongly influences task performance and employee innovativeness. Leonardi ( 2014 ) reveals that communication visibility increases meta-knowledge between organizations, which results in innovative products and services minus knowledge duplication. Aggarwal et al. ( 2012 ) interestingly reveal positive effects of negative employee posts on an organization’s reputation, given that such posts attract larger audience.

Miranda et al. ( 2015 ) suggest that diffusion of social media is based on an organization’s vision that offers a well-defined range of moves to choose from, with the freedom to improvise. Xu and Zhang ( 2013 ) regard Wikipedia as a social media platform and conclude that it improves information environment in the financial market and the value of information aggregation. Qiu et al. ( 2014 ) study prediction markets to find that users with increased social connections are less likely to invest in information acquisition from external sources. Miller and Tucker ( 2013 ) study the extent of social media managed by firms to report that most firm postings are centred on firm’s achievement and are not necessarily in clients’ interest. In summary, studies reviewed under this theme are focussed on analysing the impact of integrating social media within work roles in organizations. Effective management and utilization of social media is agreed to provoke employee activity, which helps in employee innovativeness, retention, and motivation. Studies also hint against ignoring social media engagement, which can reportedly have a negative impact on a company’s image.

4.3.4 Social Media for Marketing

Social media sites are now a huge part of marketing tactics, and the documented studies are a good showcase of the extent to which social media is being integrated in marketing strategies. García-Crespo et al. ( 2010 ) study the continuous interaction between customers and organizations, as it impacts the social web environment with implications for marketing and new product development. Goh et al. ( 2013 ) study the user and market generated content for engagement in social media brand community to find that it has a positive impact on purchase expenditures. Rishika et al. ( 2013 ) demonstrate how higher social media activity directly correlates with higher participation and customer patronage. Aggarwal and Singh ( 2013 ) find that blogs help managers with their products in the screening stage, and also offer leverage in negotiating better contract terms. Dou et al. ( 2013 ) research optimizing the strength of a network by adjusting the embedded social media features with the right market seeding and pricing strategies.

Oestreicher-Singer and Zalmanson ( 2013 ) reveal that the firms are more viable when they integrate social media in purchase and consumption experience, rather than using it as a substitute for soft online marketing. Lee et al. ( 2015b ) study the importance of social commerce in marketplace to find that Facebook likes increase sales, drive traffic, and introduce socialization in the shopping experience. Xie and Lee ( 2015 ) scan purchase records on Facebook to find that exposure to owned and earned social media activities positively impacts consumers’ likeliness to purchase brands. Chen et al. ( 2015 ) study music sales on MySpace to find that broadcasting, timing and content of the personal message has significant effect on sales. Qiu et al. ( 2015 ) study YouTube data to find that learning and network mechanisms statistically and economically impact video views. Sung et al. ( 2016 ) use Facebook data of universities and colleges across the US to show that people in the same class year or same major tend to form denser groups/networks. In a slightly different study, Oh et al. ( 2016 ) investigate the pricing models for an online newspaper, and find that charging for previously free online content has a disproportionate impact on word of mouth for niche and popular topics/articles. Susarla et al. ( 2016 ) find that social media initiatives succeed when a sustained conversation with likely adopters is maintained.

Studies within this theme focus on the role of community structure and structural patterns in using social media for marketing purposes. For successful social media implementation, it is important to effectively incorporate social computing with content delivery in the digital content industry with growing user population. Most studies identify meaningful conversations with customers as an important attribute of social media marketing. Also, identifying specific customer segments across social media site, for instance, members of a forum/group or organization, helps e-marketers to target specific customers based on demographic patterns and similar interests.

4.3.5 Social Media and Participation in Online Communities

There are many facets to developing and maintaining an online community, and user participation plays an integral role in it. Ray et al. ( 2014 ) identify that user engagement increases user intention to revisit an online community. Singh et al. ( 2014 ) analyse employee blog reading behaviour and show how reader attraction and retention are influenced by textual characteristics that appeal to reader sentiments. Butler and Wang ( 2012 ) find that changing content in an online discussion community affects member dynamics and community responsiveness, both positively and negatively. An early study on participation in online communities finds that different community commitments impact behaviours differently (Bateman et al. 2011 ). Chau and Xu ( 2012 ) develop a framework capable of gathering, extracting, and analyzing blog information that can be applied to any organization, topic, or product/service.

Goes et al. ( 2016 ) study goal setting and status hierarchy theories to find that glory-based incentives motivate users to contribute more user-generated content only before/until the goal is reached, with the contribution dropping significantly later. Khansa et al. ( 2015 ) examine Yahoo! Answers, and find that artefacts like incentives, membership tenure, and habit or past behaviour hugely influence active online participation. Tang et al. ( 2012 ) examine the concept of incentives on social media, particularly YouTube, for content contribution and find that a user is driven to contribute on social media based on their desire for revenue sharing, exposure, and reputation. Zhang and Wang ( 2012 ) use economic and social role theories in a Wikipedia context to show that in a collaborative network, the editor determines the total contribution towards collaborative work. Dennis et al. ( 2016 ) create a theoretical framework for corporate blogs and analyse Fortune 500 companies to find that a blog’s target audience and the alignment of blog content and its management significantly impact the visibility of that blog. Most of the studies under this theme focus on analyzing data on blogs. They highlight the importance of word of mouth, which is closely associated with user satisfaction. It also emerges from these studies that user engagement and consequent satisfaction play parallel and mediating roles within such online communities.

4.3.6 Risks and Concerns with the Use of Social Media

Social media and its associated risks have captured the attention of many authors. A very early study by Griffiths and Light ( 2008 ) focuses on the problem of media convergence, whereby a gaming website includes social media features, putting vulnerable young audience at the risk of scamming. An Australian study suggests that many users are unaware of the potential risks of disclosing personal information on social media site, or consider themselves as low risk targets (Tow et al. 2010 ). Krasnova et al. ( 2010 ) find that the ease of forming and maintaining relationships on an enjoyable social platform motivates users to disclose personal information. Their study shows that user trust in a service/network provider, and privacy control options on a networking site greatly dismiss user perceptions of associated risk. Vishwanath ( 2015 ) finds that farcing attacks on Facebook occur at two levels – victim to phishers with phony profiles and victim to phishers soliciting personal information directly from them.

To combat the privacy problem of photos, videos, and other content posted online, Fogués et al. ( 2014 ) developed a Best Friend Forever tool that automatically distinguishes friends on a user’s profile by assigning individual values based on relationship ties. Zhang et al. ( 2016 ) find that incorporating non-verbal features of reviewers can massively improve the performance of online fake review detection models. Gerlach et al. ( 2015 ) find that user perception of privacy risks has a mediating effect on the relationship between policy monetization and user willingness to share information. Burtch et al. ( 2016 ) analyse a large online crowd funding platform and report that when campaign contributors control/conceal visibility from public display, there is a negative impact on subsequent visitor’s conversion likelihood and average contributions. In a different study, Choi et al. ( 2015 ) find that information dissemination and network commonality has a high impact on individual’s perception of privacy invasion and relationship bonding that impedes transactional and interpersonal avoidances.

Studies reviewed here discuss a social contagion effect of risks associated with social media use. Recent studies (2014–2016) suggest educating audiences about the threats associated with the extent of personal information being disclosed on social media sites. They recommend government agencies to keep the users informed, and the social media sites to control some of their security features. It is necessary to define and control privacy settings across these many existing social networks.

4.3.7 Negative Stigma Attached to Social Media Use

Some studies suggest that there is a negative stigma associated with the use of social media in the workplace. In a typical case study, Koch et al. ( 2012 ) analyze three employee layers in an organization to find that new hires (users of social media sites) showed improved morale and employee engagement, some middle managers (non users) were frustrated and experienced isolation, while the senior execs were wary of social media use. In a contrasting case, Cao et al. ( 2015 ) suggest that social media has the potential to build employees’ social capital to positively influence their knowledge integration. In discussing the impact of social media on organizational life, Koch et al. ( 2013 ) find that conflicts can stem between workplace values and the values these employees ascribe to social media.

In a gender-based study on social network facilitated team collaboration, Shen et al. ( 2010 ) found that the collective intention in men was influenced by positive emotions, attitude and group norms, while the collective participation intention in women was affected by negative emotions and social identity. Huang et al. ( 2015 ) debate the concept of communicational ambidexterity to understand the conflicting demands of managing internal organization communication in contrast to open and distributed social media communication. Wu ( 2013 ) suggests information-rich networks enabled by social media tend to drive job security and employee performance. Lu et al. ( 2015 ) use the social network theory to conclude that structural and cognitive dimensions of social relationships positively impact job performance. Ali-Hassan et al. ( 2015 ) show social and cognitive use of social media has a positive influence on employee performance, while hedonic use of social media leaves a negative impact on routine performance.

These reviewed studies showcase that social networking encourages shared language and trust between employees in a workspace. Another emerging suggestion highlights that organizations should exercise policy, and use socialization and leadership-based mechanisms to counter any problems resulting from differing workplace values. Some of these studies show interest in the cognitive side of social ties that positively nurture social relationships and innovation performance.

4.3.8 Social Media and Value Creation

Studies in the extant literature have particularly focussed on the aspect of value creation within online communities. As Ridings and Wasko ( 2010 ) have observed, an online discussion group/community is a direct product of its social and structural dynamics. Porter et al. ( 2013 ) investigate firm value and find that a sponsor’s efforts are stronger with positive and direct effect on trust building. Luo et al. ( 2013 ) suggest that social media has faster predictive value than conventional online media, and that the embedded metrics like consumer ratings are leading indicators of a firm’s equity. Hu et al. ( 2015 ) develop a formative model with an aggregate online social value construct and identify factors to increase user benefits and satisfaction, ensuring customer retention via continued usage of online services. In a public organization study focussing on social networking system, Karoui et al. ( 2015 ) suggest that differing perceptions of social capital can result in actors adopting differing strategies for holding their social position within an organization. Barrett et al. ( 2016 ) find that value creation in online communities expands beyond the dyadic relationship between a firm and the community to include a more intricate relationship involving stakeholders of a wider ecosystem. Dong and Wu ( 2015 ) use data from Dell and Starbucks and find substantial evidence for online user innovation-enabled implementation increasing firm value. Overall, the studies on social media and value creation emphasize on influence of social and structural interplay on sustainability, which is visible over longitudinal examination of their relationship to one another.

4.3.9 Role of Social Media During Critical/Extreme Events

Certain authors are more interested in micro-blogging used at the time of critical/extreme events. In an attempt to filter real time news/updates from irrelevant personal messages and spam, Cheng et al. ( 2011 ) propose analysis of information diffusion patterns for a large set of micro-blogs that update emergency news. They claim that their approach (using Twitter data) outperforms other benchmark solutions to offer diverse user preferences and customized results during critical events. Cheong and Lee ( 2011 ) use Twitter data to propose a framework that is useful for Homeland Securities and Law enforcement agencies to record and respond to terror situations. Oh et al. ( 2013 ) also study Twitter data from three extreme events to find that information without any clear source is at the top, personal involvement comes second, with anxiety at third place in the list of rumour causing factors during social crisis events. Wang et al. ( 2014 ) affirm that news spreads widely through online portals. They find that news first posted even on a small news portal can be picked and reposted by a major news portal, forming a hotspot event for the news to rapidly spread over the Internet.

Lee et al. ( 2015a ) performed negative binomial analysis of the 2013 Boston marathon tragedy Tweets to find that follower numbers, reaction time, and hash tagging significantly affected the diffusion of Tweets. Oh et al. ( 2015 ) analysed Twitter data from the 2011 Egypt revolution and found that hash tags played a critical role in gathering information and maintaining situational awareness during such politically unstable phases. Ling et al. ( 2015 ) undertake a qualitative study of 2011 Thailand flooding data to conclude that social media can offer a community: structural, resource, and psychological empowerment to achieve collaborative control and collective participation. In summary, studies since 2011 have been particularly examining Twitter data, and have derived significant insights on their positive effect during critical/extreme events.

4.3.10 Social Media for Help/Support

Some users post updates on social media with an aim to seek help/support from online communities. Maier et al. ( 2015a ) find that such posts cause social overload for other users, and the psychological consequences include feelings of exhaustion, low user satisfaction, and high intentions of reducing/stopping the use of social media sites. Yan et al. ( 2015a ) find that healthcare traits of patients help them establish social connections online, which is influenced by their cognitive abilities. Spagnoletti et al. ( 2015 ) develop a user utility model for integrating social media in personalized elderly healthcare that is capable of challenging traditional organizational boundaries to transform the internal and external stakeholder engagement. Yan and Tan ( 2014 ) propose a partially observed Markov decision process model to find sufficient evidence suggesting emotional support is most significant in improving patient health. Kallinikos and Tempini ( 2014 ) study the ups and downs of having a large unsupervised social network based on patient self-reporting for gathering and examining data on patients’ health.

Limited number of studies has been recorded for this theme. These studies are fairly recent suggesting a new emerging trend, where health/support based communities are being formed. The expanse of such communities seems to be largely dependent on the information processing capacity and the range of social ties that the members of such networks can handle. Using social media to bring together people with similar health conditions suggests that informational and social support can have varying influence on patient health.

4.3.11 Public Bodies and Social Media Interaction

User-generated content from social media is becoming one of the important information channels across public administrative bodies and political contexts. Baur ( 2017 ) has developed a MarketMiner framework that massively improves the utilization of multi-source, multi-language social media content, which can be applied to areas such as open government. Rosenberger et al. ( 2017 ) use abstraction-based modelling to conceptualize the data structure, and conclude that wrapping social network application programming interfaces allow mutual integration of most user activities. Gonzalez-Bailon et al. ( 2010 ) show that political discussions in online networks are larger and deeper compared to other networks. Ameripour et al. ( 2010 ) analyse the restricted Iranian social networks, subject to surveillance and censorship to find that Internet conviviality is not an independent variable with deterministic outcomes, but is a technology shaped by economic and political forces. Although, not published in the list of journals included in this review, Kapoor and Dwivedi ( 2015 ) provided a detailed discussion on how social media was used intensively to transform electoral campaigns during India’s last general election. Similar use has also been reported in other contexts (for example, US presidential elections) by other studies.

Except one study (that is, Ameripour et al. 2010 ), the remaining reviewed under this category are very recent (2015–2016). These studies suggest the use of social media for increasing public engagement and transparency. Most of these studies used technical frameworks and modelling techniques to identify communication clusters and structures to derive insights relevant to open government and political campaigns.

4.3.12 Traditional v/s Social Media

Another set of studies investigate the differences between traditional and social media. A very early study by Wattal et al. ( 2010 ) compares the big money tactics for political campaigning with social media campaigning to reveal that Internet and the blogosphere can majorly influence campaigning and election results. Dewan and Ramaprasad ( 2014 ) examine the importance of new and old media within the music industry; they find radio positively and consistently affecting sales of songs and albums, and sales displacement from free online sampling overpowering positive word of mouth on sales. Miranda et al. ( 2016 ) compare traditional and social media to suggest that there are evils associated with the societal benefits of social media, and mass media has a detrimental effect on public discourse.

4.3.13 Testing Pre-Established Models

Some studies in literature restrict focus to pre-established models and relationships for evaluating varied aspects of social media. Fang et al. ( 2013 ) apply social network theories to suggest positive social influence on adoption probabilities. Levina and Arriaga ( 2014 ) use Bourdieu’s theory to explain the role of status markers and external sources in shaping social dynamics. Bharati et al. ( 2014 ) combine institutional theory and organizational innovation, whereby institutional pressures significantly predict absorptive capacity. Kekolahti et al. ( 2015 ) use Bayesian networks to indicate the decrease in perceived importance of communication with increase in age. Chang et al. ( 2015 ) integrate social distance with clustering methods to show shorter social distance results in satisfactory trust. Chung et al. ( 2017 ) employ the Technology Acceptance Model, and find positive effects between traveller readiness and ease of using geo-tagging. Zhao et al. ( 2016 ) use theory of planned behaviour and attribution theory to find that virtual rewards for sharing knowledge online undermine enjoyment. Yu et al. ( 2015 ) use the causation and heuristic theories to find that affect influences self disclosure indirectly by adjusting perceived benefits. Stanko ( 2016 ) employs Innovation Diffusion Theory, and finds that community interaction influences innovations that are used to aid a further innovation.

5 Discussion

In reviewing the publications gathered for this paper, commonalities have been observed in the myriad aspects of social media chosen for investigation. While many studies focussed their attention on understanding the behaviours of social media users, the others examined entrepreneurial participation and firm behaviour. A number of studies have focussed on the content being posted in online communities, several of which report on the repercussions of some of this content being used as an awareness medium during critical events and tragedies. Interesting revelations were made by authors studying the use of social media as a platform to render and/or receive help or support, and its incorporation in the field of healthcare and public administration. Value creation and the ill-effects associated with the use of social media at the workplace were also discussed. Several studies chose to test previously established hypotheses and models, while others compared traditional media with social media. Prior research has also provided insights into how firms have been using social media to market their products and services. These strategies run in parallel with the reviews and recommendations posted by users on social media sites, which have also received considerable attention in the literature. In summary, given that different types of social media platforms are emerging, and different consequences are associated with their use, research in this field will continue to evolve. This is also evidenced by the increased number of publications related to usage and impact over the past five years.

Social media platforms have essentially redefined the ways in which people choose to communicate and collaborate. An online community is a socio-technological space where a sense of communal identity drives engagement, which, in turn, enhances satisfaction (Ray et al. 2014 ). Intriguingly, social media are facilitating the emergence of virtual knowledge communities and self help networks. These web-based arrangements allow medical practice and research to access patient experience on a daily basis, which was not possible earlier. However, since research in this area is still in its early stages, it is difficult to assess the social complexity involved (e.g., stability of a networking platform that brings together patients with medical experts) in the process (Kallinikos and Tempini 2014 ).

Firms are recognizing social media as a prominent indicator of equity value that not only improves short-term performance, but also brings about long-term productivity benefits (Luo et al. 2013 ). The reviewed studies suggest that incorporatin social media in firms increases meta-knowledge (who’s who in an organization and who does what), which helps avoid knowledge duplication and promotes new ways of managing work (Leonardi 2014 ). Active management of social media has been observed to be more effective when those inside rather than outside a firm are engaged (Miller and Tucker 2013 ).

A specific line of research focuses on consumers, who substantially rely on online reviews before making any purchase decision. The research papers reviewed in this study exhibit diversity in studying authenticity of reviews for travel sites, social bookmarking and review sites, movie ratings, car manufacturing, and social media check-ins. Studies concur that there has been an exponential increase in the number of fake reviews, which is severely damaging the credibility of online reviews and putting business values at risk (Zhang et al. 2016 ). Some studies have also empirically identified consumers’ social media participation as a key metric contributing to the profitability of a business (Rishika et al. 2013 ). There evidently exists a direct correlation between consumer engagement on social media sites and their shopping intentions, which makes the issue of legitimate reviews all the more important for businesses and consumers. Although some studies have proposed models and algorithms that claim to filter authentic reviews from the rest, there is no single and straightforward solution reported yet that can fully combat this problem.

The issue of negative posts has received considerable attention in the literature. Prior research suggests that, overall, the impact of negative posts or electronic word of mouth is much higher than the positive ones that increase readership (Aggarwal et al. 2012 ). This problem is also prevalent in organizations. According to the studies reviewed here, organizations either prohibit employees from posting controversial content online, or employees themselves refrain from doing so, fearing negative repercussions. The same employees also share positive posts, and the adverse effect of the few negative posts is offset by positive ones. It is in a firm’s interests to encourage free will enterprise blogging to break down knowledge silos and yield higher employee productivity (Singh et al. 2014 ).

Businesses looking to monetize online content and social search rely heavily on substantial understanding of consumer behaviour in terms of their interaction and participation in social settings (Susarla et al. 2012 ). As consumers gain access to social platforms that offer free content consumption without an obligatory payment, the relationship between sampling and sales becomes all the more important (Dewan and Ramaprasad 2014 ). There is much research supporting the belief that online word of mouth has a critical role to play in a firm’s overall performance, and introducing a pay-wall (for previously free content) can significantly reduce the volume of word of mouth for popular content in comparison to niche content (Oh et al. 2016 ). Determining consumers’ social influence in an online community is of critical interest to managers, who seek to gain some leverage from the potential of social media (Shi et al. 2014 ). Some researchers find it difficult to distinguish social influence from users’ self selection preferences. From an analysis point of view, it then becomes necessary to separate factors affecting user membership in a social network from various types of social influence (Susarla et al. 2012 ).

The findings on the use of social media in emergencies suggests that a general user response in an online community is very different from that during a crisis, as those responses then become more reflexive. It has been observed that in times of crisis, lack of information sources coupled with too many situation reports being shared by the users of a social media platform can precipitate a rumour mill. It thus becomes incumbent on emergency responders to release reliable information, whilst trying to control collective anxiety in the community, to suppress the rumour threads (Oh et al. 2013 ). Furthermore, security concerns are increasingly common with involuntary online exposure on social media, and research on this subject suggests that information dissemination with network commonality affects privacy invasion and user bonding (Choi et al. 2015 ). It has been learnt that an individual’s or firm’s decision to withhold information in the interest of privacy can have both positive and negative effects on their utility (Burtch et al. 2016 ).

In reviewing the 132 publications on social media and social networking, it was observed that many studies relied primarily on social exchange theory, network theory and organization theory. Table  3 , shown below, lists other theories that have been used by at least two publications. There were several other theories that were used by at least once, including social role theory, game theory, structural holes theory, management and commitment theories, institutional theory, deterrence and mitigation theories, and self determination and self categorization theories. It is noteworthy that dominant IS adoption theories such as Unified Theory of Acceptance and Use of Technology (Dwivedi et al. 2017b , c ; Rana et al. 2017 ; Venkatesh et al. 2003 ), Technology Acceptance Model (Davis 1989 ) and Innovation Diffusion Theory (Kapoor et al. 2015 ) are less widely utilised.

In addition, our review of the literature on social media identified dominant research methods employed by scholars. Qualitative, quantitative, and mixed methods were used by most of these studies. Closer scrutiny of the 132 publications reviewed in this study revealed the multitude of techniques applied for gathering data. Quantitative methods employed in these studies mostly adopted analytical techniques and surveys (Table  4 ). On the other hand, publications using qualitative methods mainly used case studies and interviews to gather the required data (Table 4 ). As expected, studies employing mixed methods used a combination of analytical and conceptual techniques, alongside surveys and content analysis (Table 4 ). Table 4 summarizes the various research approaches used by publications in our corpus.

The reviewed publications were also analyzed to determine the nature of the social network that were studied. Precisely 46 websites emerged, with Facebook, online communities, Twitter, Blogs and YouTube being most frequently targeted. Networks analysed by at least two or more studies have been identified in Table  5 . The other networks that received attention from the reviewed publications include Ebay, Flickr, Flixster, Gtalk, microsoft, MSN Space, Patientslikeme, New York Times, TripAdvisor.com , and Boxofficemojo.com . Studies also focussed on websites related to online news, Q&A websites, discussion groups and forums, online radio and television, and medical sites such as Webmd.com .

5.1 Limitations and Future Research Directions

Studies, such as the one by Cheung et al. ( 2012 ), that examine aspects of popular websites, warn against consumer perceptions being under the influence of brand equity of those websites. They recommend exercising caution while generalizing such findings in the context of other websites (Cheung et al. 2012 ). Rosenberger et al. ( 2017 ) identify a similar problem with relying on publicly available data, in that the underlying abstraction makes findings valid only for the specific social media site that was analyzed, whilst significantly restricting its generalizability to other sites. In a similar vein, other studies (Krasnova et al. 2015 ; Khan and Jarvenpaa 2010 ; Tow et al. 2010 ) have acknowledged the limitation of restricting their research to a single social media site, and recommend future researchers to adopt a cross-platform perspective for drawing significant inferences.

Mettler and Winter ( 2016 ) suggest that there is a paucity of studies on Enterprise Social Systems because of its novelty, and urge researchers to fill this void. Turel and Serenko ( 2012 ) identify the lack of conceptualization in the notion of technology addiction; they recognize that the process of defining it is still in the early stages, and is being debated across communities. For researchers interested in examining aspects of Twitter, Cheng et al. ( 2011 ) recommend incorporating the location metric focused on Twitter’s geo location feature allowing users to trace the latitude and longitude of Tweets. Another recommendation for Twitter related studies comes from Benthaus et al. ( 2016 ), where they suggest researchers should study user involvement differently, based on how often users choose to ‘like’ the content of a company. As for use of social media for marketing in firms, the literature has restricted focus to the resulting marketing benefits, with limited evidence supporting the effectiveness of social platforms for enhancing employee communications (Miller and Tucker 2013 ).

For behavioural studies, researchers need to be wary of the fact that motivation for users to adopt social media is different, often contingent on their culture (Chiu and Huang 2015 ; Shen et al. 2010 . It is also important to note that behavioural reactions are susceptible to change over time, and changing habits have a role to play (Chiu and Huang 2015 ). Longitudinal research is thus always expected to offer a better understanding of the research problem when the intended behavioural reactions transfer into behaviour with time (Maier et al. 2015a ). In studying online reviews and recommendations, researchers can assume that these reviews are independent of one another and remain static over time; however, Zhang and Piramuthu ( 2016 ) suggest that this may not be true and future researchers should now concentrate on how this has evolved, and if herding behaviour exists on such online platforms. In studying behaviours, it has also emerged that users develop discontinuance intentions after continuance intentions, with the latter never being completely replaced by the former. Turel ( 2015 ) thus recommends studying the initiation of discontinuance intentions, whilst identifying the factors leading to its dominance and actual discontinuance attempts.

Matook et al. ( 2015a ) identify that there is a need to study the aspect of trust formation between individuals on social media, where no personal relationships exist (unlike sites such as Facebook). Chung et al. ( 2017 ) identify that researchers often associate the use of certain social media with young users (for instance, Maier et al. 2015b ), and fail to study the usage perceptions across various ages (Vishwanath 2015 ). Van Osch and Steinfield ( 2016 ) suggest that future researchers should explore the potential of Enterprise Social Media to gain insights into the tools that support disentanglement of team boundary spanning. Finally, researchers have established that the lifecycle of information and communication technologies tend to be emancipatory in their infancy but eventually evolve into hegemonic tools. They warn social media policymakers to be wary of reproducing this pattern with digital media; the recommendation is to involve more citizens in the development of Internet governance framework, rather than resting decisions with the members of political or economic power (Miranda et al. 2016 ).

6 Conclusions

This paper discusses the findings of 132 publications contributing to the literature on social media. Multiple emergent themes in this body of literature have been identified to enhance understanding of the advances in social media research. By building on empirical findings of previous social media research, many new studies have been successful in theorizing the nature of most social media platforms. User-generated content allows collective understanding, which is a massive machine-human knowledge processing function capable of managing chaotic volumes of information. Some key conclusions relevant to stakeholders, including researchers, have been identified here.

Social media technologies are no longer perceived just as platforms for socialization and congregation, but are being acknowledged for their ability to encourage aggregation.

In reviewing the 132 publications on social media and social networking, it was observed that most studies used social exchange theory, network theory and organization theory to support their studies.

Facebook, online communities, and twitter are the three most popular networks targeted by publications in the field of social media research.

Publications in 2011 were still reporting user-generated content as a new type of online content. However, the last six years have seen tremendous scholarly progression in discussing the many applications of social networking, highlighting the highs and lows associated with its use.

Majority of the publications reviewed in this study are focussed on behavioural side of social media, reviews, and integration of social media for marketing and organizational purposes.

Many publications in the year 2013 concentrated their efforts in investigating the very popular aspect of reviews and recommendations on social networks.

Publications have become more focussed on studying social commerce across networking sites, particularly, Facebook, MySpace, YouTube and so on between 2015 and 2016.

Publications have not shown much interest in support-seeking posts and negative stigma attached to social media use after the year 2015.

Most studies unanimously acknowledge social media for its information sharing and information exchange capabilities, with a focussed group of studies recognizing its effectiveness during natural disasters and critical events.

Almost all publications studying information sharing during natural disasters and critical events focus on Twitter data.

Publications on administration and political contexts were particularly found in 2010 and 2016, with no interest expressed in these contexts between 2011 and 2015.

With information systems now expanding beyond organizational peripheries to become a part of the larger societal context, it is important for strategic information systems research to delve into the competitive setting of dynamic social systems. Online communities are introducing extrinsic rewards that do not limit users’ intrinsic motivations. Research on such communities should expand to study the interplay between extrinisic and intrinsic rewards, particularly in terms of their ability to cultivate and sustain users’ intrinsic motivations. From an organizational perspective, research on social media should move past the conventional dyadic view of the relationship between an online community and a firm, and focus on reconceptualising online users as an ecosystem of stakeholders. Social media has re-established the dynamics between organizations, employees, and consumers. Given the rise in number of publications focussing on workplace setting since 2014, future researchers should aim to analyze stakeholders’ potential in adopting social media tools to successfully accomplish their work goals. As for the limitations of this collective review, publications reviewed here were limited to only nine journals. This potentially means studies with significant contributions to social media literature published in other journals have been overlooked. Future researchers can look to overcome such exclusions and focus on the overall review of literature on social media platforms. Future reviews may focus on reviewing articles published in a larger number of IS journals related to a specific type of social media (i.e. social networking sites, blogs), or specific issues related to social media use, such as information load, stress, and impact on productivity. Despite these limitations, our study provides a comprehensive and robust intellectual framework for social media research that would be of value to adacemics and practitioners alike.

TITLE: (“Social Media” or “social networking” or “facebook” or “linkedin” or “instagram” or “twitter”)

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Kapoor, K.K., Tamilmani, K., Rana, N.P. et al. Advances in Social Media Research: Past, Present and Future. Inf Syst Front 20 , 531–558 (2018). https://doi.org/10.1007/s10796-017-9810-y

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Published : 06 November 2017

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DOI : https://doi.org/10.1007/s10796-017-9810-y

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• Published: 07 September 2022

The mental health and well-being profile of young adults using social media

• Nina H. Di Cara 1 , 2 ,
• Lizzy Winstone 1 ,
• Luke Sloan 3 ,
• Oliver S. P. Davis 1 , 2 , 4   na1 &
• Claire M. A. Haworth 4 , 5   na1  

npj Mental Health Research volume  1 , Article number:  11 ( 2022 ) Cite this article

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• Human behaviour
• Interdisciplinary studies
• Medical research
• Psychiatric disorders

The relationship between mental health and social media has received significant research and policy attention. However, there is little population-representative data about who social media users are which limits understanding of confounding factors between mental health and social media. Here we profile users of Facebook, Twitter, Instagram, Snapchat and YouTube from the Avon Longitudinal Study of Parents and Children population cohort ( N  = 4083). We provide estimates of demographics and mental health and well-being outcomes by platform. We find that users of different platforms and frequencies are not homogeneous. User groups differ primarily by sex and YouTube users are the most likely to have poorer mental health outcomes. Instagram and Snapchat users tend to have higher well-being than the other social media sites considered. Relationships between use-frequency and well-being differ depending on the specific well-being construct measured. The reproducibility of future research may be improved by stratifying by sex and being specific about the well-being constructs used.

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Introduction.

The trails of data left online by our digital footprints are increasingly being used to measure and understand our health and well-being. Data sourced from social media platforms has been of particular interest given their potential to be used as a form of ‘natural’ observational data about anything from our voting intentions to symptoms of disease. There is not a single, widely agreed definition of the term ‘social media’ 1 , but for the purposes of this study we understand it to be a broad category of internet-based platforms that allow for the exchange of user-generated content by ‘users’ of that platform 2 . Both the huge volumes of data available on such platforms, and their increasing uptake across the population 3 have led to two main fields of interest in the intersections of social media and mental health. These are the prediction of mental health and well-being from our online data 4 and, somewhat reciprocally, the influence of social media on our mental health, particularly in the case of children and young people 5 , 6 . These fields both ask fundamental questions about the mental health and well-being of social media users, to either understand the ways our mental health influences our social media behaviour, or how our social media behaviours influence our mental health.

Across both contexts a wide range of psychological outcomes have been studied, including predicting suicide at a population-level 7 and individually 8 , mapping the influences of social media platforms on disordered eating 9 and self-harm 10 , understanding the impacts of cyberbullying through social media platforms 11 , 12 , and even ethnographic research into online support networks 13 . As highlighted in a recent review which considered research on the relationship between social media use and well-being in adolescents 14 , there has tended to be an inherent assumption that social media is the cause of harm when examining the effect of social media on our health. However, recent investigations such as those by Orben and Przybylski 15 , 16 and Appel and colleagues 17 illustrate that the role of social media in causing harm may be over-estimated. It seems likely that there is some reciprocal relationship between mental health and social media, that requires longitudinal research studies to begin to understand the complexity, coupled with large representative samples to explore the heterogeneity 18 , 19 . Further, there is increasing attention on the role of within-person effects that see impact change between contexts 20 , 21 , as well as individual differences 22 . Meanwhile, attention has also been drawn to the comparative lack of investigation into the potential benefits of social media, such as access to peer support and the ability to readily connect with friends and family, or into the psychological well-being of social media users as opposed to focusing on pathology. Similarly, most psychological prediction tasks using social media focus on predicting illness rather than wellness 4 , 23 .

Regardless of the direction of interest in the relationship between social media and psychological outcomes, researchers face common challenges, with one of the primary issues being a lack of high-quality information on the characteristics of the whole population of social media users 24 . Valuable demographic information on social media users in the United States is regularly produced by the Pew Research Centre 25 , but often researchers rely on algorithmic means to make predictions about the demographics of the groups they study online if they are not recruiting a participant sample whose demographics are known and can be recorded 4 , 24 , 26 . What we do know about social media users is that they are not homogeneous. The demographic features of populations using them vary across platforms and do not tend to be consistent with the characteristics of the general population 25 , 26 , 27 , 28 . This work on the demographic context has been important in understanding the samples that can be drawn from social media platforms, but there remains a lack of information about other characteristics of social media users that are relevant to study outcomes, including mental health and well-being. Consequently, attempts to compare user well-being and mental health between platforms may be unknowingly confounded by differences in the mental health profile of each individual platform. Mellon and Prosser 28 investigated this form of selection bias with respect to differences in political opinion between Facebook and Twitter, and noted the potential for study outcomes to be biased when the outcome variable of interest is associated with the probability of being included in the sample 29 . This also has implications for our assessment of mental health and well-being classification algorithms 30 . For instance, if using Twitter data to classify depression in a random sample of users how many of these users should we expect to be depressed? Should we expect to find more depressed users on Facebook or Instagram? This bench-marking would allow the research community, who frequently face the challenge of establishing reliable ground truth in social media research, to contextualise the sensitivity and specificity of developed models 4 , 24 .

This study aimed to address the gap in the availability of high-quality descriptive data about social media users by describing social media use in a representative UK population cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC) 31 . We aimed to profile the users of the social media platforms Facebook, Instagram, Twitter, Snapchat and YouTube by considering a range of mental health and well-being measures that are regularly studied, with the objective of better characterising social media users against variables of interest to researchers. These measures included disordered eating, self-harm, suicidal thoughts, and depression as well as positive well-being outcomes which are sometimes neglected in the context of social media research 14 , 16 , 22 like subjective happiness, mental well-being and fulfilment of basic psychological needs. In answering our research questions we also sought to illustrate how cross-sectional data from a representative population cohort can provide meaningful contextual information that informs the way we interpret past and future research about social media users and their mental health. Unlike other studies using cross-sectional data 14 we had no intention of exploring causal questions, but aimed to address unanswered questions of who social media users are, and whether selection bias across platforms may have the potential to unintentionally bias outcome statistics about mental health and well-being.

Specifically, our research questions were:

Are there demographic differences in patterns of social media use (e.g. frequency)?

Are there demographic differences in the user groups of different social media platforms?

Are there differences in the mental health and well-being of those using social media sites at different frequencies?

Are there differences in the mental health and well-being of user groups of different social media platforms?

Sample description

The sample for this study is drawn from the Avon Longitudinal Study of Parents and Children (ALSPAC) 31 , 32 , 33 . Pregnant women resident in Avon, UK with expected dates of delivery from 1st April 1991 to 31st December 1992 were invited to take part in the study. The initial number of pregnancies enrolled was 14,541. Of these initial pregnancies, 13,988 children were alive at 1 year of age. When the oldest children were ~7 years of age an additional 913 children were enrolled. The total sample size for ALSPAC of children alive at one year of age is 14,901. However, since this time there has been a reduction in the sample due to withdrawals, deaths of those in the cohort and also people simply being lost to follow-up. As such the exact number of participants invited to each data collection activity changes with time. Please note that the ALSPAC study website contains details of all the data that is available through a data dictionary and variable search tool ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ). Study data were collected and managed using REDCap electronic data capture tools hosted at the University of Bristol 34 .

The analysis presented in this study is based on a sub-sample of 4083 participants who responded to a self-report questionnaire at a mean age of 24 years old in 2016/17. The survey was sent to 9211 currently enrolled and contactable participants, of whom 4345 (47%) returned it. To maintain a consistent sample throughout the following analyses we considered the 4083 observations with complete cases for questions related to self-harm, suicidal thoughts, disordered eating, and social media use, and without the respondents who said that they ‘didn’t know’ whether they had a social media account ( n  < 5); no respondents stated that they did not have a social media account. As well as the survey at age 24, we considered the responses by those in our main sample to a survey one year previously, at age 23, which collected the well-being measures and the Moods and Feelings Questionnaire, matched to their social media use responses at age 24. This resulted in a sub-sample of 2991 participants who had responded to both surveys. Table 1 gives a comparison of the demographic breakdowns across these samples.

This study considered the participants’ responses to a range of mental health and well-being measures, as well as demographic data. A brief overview of each of the measures used is given below.

Throughout this paper, we used Male and Female to refer to the participant’s assigned sex at birth. Participant ethnicity was reported by their parent/s, and is available in the data as White , Ethnic Minority Group , or Unknown , where Ethnic Minority Group was only available as one group rather than broken down into specific ethnicities. There were two variables relevant to socio-economic status. The first was whether the participant had achieved an A Level or equivalent qualification by age 20, the second was their parents’ occupation. Parental occupation was measured using the Registrar General’s Social Class schema 35 , and was collected prior to the birth of the index cohort; we took the higher occupational class of the participant’s parents where available and grouped the overall schema of six categories into those in manual work , and those in non-manual work .

Social media use was measured using three questions. These were: (1) Do you have a social media profile or account on any sites or apps? with possible responses of ‘Yes’, ‘No’ or ‘Don’t know’; (2) Given a list of social media sites, Do you have a page or profile on these sites or apps, and how often do you use them? , where the social media sites were listed and response options were ‘Daily’, ‘Weekly’, ‘Monthly’, ‘Less Than Monthly’ or ‘Never’; (3) How often do you visit any social media sites or apps, using any device? with response options being ‘More than 10 times per day’, ‘2 to 10 times per day’, ‘Once per day’ or ‘Less than once per day’. Here, the definition of ‘social media sites’ in questions (1) and (3) was left to the participant to interpret, whereas in (2) a specific list was provided. In the following analyses, we have summed responses for the use frequencies per platform from question (2) so that ‘Weekly’, ‘Monthly’ and ‘Less than monthly’ are combined to represent ‘Less than daily’.

Depressive symptoms were measured using the short Mood and Feelings Questionnaire (MFQ) 36 , a 13-item scale that has been validated for measuring depressive symptoms in adolescents 37 and in young adulthood 38 . It asks respondents to rate statements, such as I cried a lot and I thought nobody really loved me , as Not true , Sometimes or True based on how they felt over the past two weeks. Missing items were filled with the mode of the individual’s other responses, provided 50% or more of the items were completed. Scores range from 0 to 26, with a higher score indicating more severe depressive symptoms 37 . Here we applied a cut-off score of 12 or above as indicating depression 38 .

Suicidal thoughts were assessed with the question Have you ever thought of killing yourself, even if you would not really do it? with those who indicated that they had ‘within the past year’ being included. Similarly, intentional self-harm was assessed by asking if participants had hurt [themselves] on purpose in any way and we included those who said this had happened at least once within the last year.

Disordered eating was a composite variable that included participants who indicated that they had been told by a healthcare professional that they had an eating disorder (anorexia nervosa, bulimia nervosa, binge eating disorder or another unspecified eating disorder). Participants were also included if they indicated they had engaged in any of the following behaviours at least once a month over the past year with the intention of losing weight or avoiding weight gain: fasting, throwing up, taking laxatives or medication. This classification of disordered eating followed a similar methodology to that used by Micali and colleagues 39 .

Well-being was measured using seven questionnaires. The Warwick Edinburgh Mental Well-being Scale (WEMWBS) is a fourteen-item questionnaire that has been validated for measuring general well-being in the general population 40 , 41 , as well as in young people 42 , 43 . It asks respondents to rate statements such as I’ve been dealing with problems well and I’ve been feeling cheerful , on a five-point Likert-type scale. The total score is between 14 and 70. All items in the WEMWBS are positively worded, and it is focused on measuring positive mental health.

The Satisfaction with Life Scale 44 , 45 is five-item questionnaire designed to measure global cognitive judgements of satisfaction with one’s life, which includes statements such as If I could live my life over, I would change almost nothing . Each question uses a seven-point Likert-type measure and the total score is between 5 and 35. The Subjective Happiness Scale 46 is a four-item questionnaire based on seven-point Likert-type questions, with the overall score being a mean of the four questions, lying in the range of 1 to 7. Respondents answer questions such as whether they consider themselves to be more or less happy than their peers.

The Gratitude Questionnaire (GQ-6) is a six-item measure that uses a seven-point Likert-type scale to assess individual differences in proneness to experiencing gratitude in daily life 47 . This scale includes statements such as I have so much in life to be thankful for and I am grateful to a wide variety of people . Each score is summed to a total between 6 and 42. The Life Orientation Test (LOT-R) is a measure of dispositional optimism that has ten items asked on a 5-point Likert-type scale 48 , though only four of these items are ‘filler’ questions that do not contribute to the final score. The overall score is in the range of 0 to 24, and items that contribute to this include In uncertain times, I usually expect the best and I hardly ever expect things to go my way .

The Meaning in Life questionnaire has 10 items designed to measure two dimensions of meaning in life: (1) Presence of Meaning (how much respondents feel their lives have meaning), and (2) Search for Meaning (how much respondents strive to find meaning and understanding in their lives) 49 . Statements include I understand my life’s meaning in the Presence sub-scale, and I am looking for something that makes my life feel meaningful in the Search sub-scale. Respondents answered each item on a 7-point Likert-type scale, with the two sub-scales scored in total between 5 and 35.

The psychological constructs of autonomy, competence and relatedness associated with self-determination theory were measured using the Basic Psychological Needs in General (BPN) questionnaire 50 . This questionnaire has 21 seven-point Likert-style questions with the final score for each of the three sub-domains being the mean of the responses for that sub-domain. As such each of autonomy, competence and relatedness were scored overall from 1 to 7. Example items include People in my life care about me and I often do not feel very capable .

For all measures missing items were filled with the person-level average, provided that half or more of the items were completed. All of the well-being measures listed were scored in a positive direction, where higher scores indicate higher alignment with the construct being measured.

The descriptive statistics were calculated using the R programming language (v4.0.1) 51 in RStudio (v1.3), primarily using the tidyverse (v1.3.0) package 52 for data manipulation and ggplot2 (v3.3.1) 53 for visualisation. A reproducible version of the manuscript and supporting code can be found from the Code availability statement.

Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. The full list of ethical approval references for ALSPAC can be found on their website ( https://www.bristol.ac.uk/alspac/researchers/research-ethics/ ).

Demographics

We first consider the demographics of social media users across different frequencies of use, and across the five social media platforms: Facebook, Twitter, Instagram, Snapchat and YouTube. These are both taken from the main sample, as described in our ‘Methods’. Table 2 presents the frequency that participants reported using any social media sites each day, based on sex, ethnicity, education, and their parents’ occupational group.

Table 3 gives the percentage of participants from each demographic group who reported being a user of each platform with any use frequency.

The breakdown of every demographic by frequency of use on each platform is provided in full in Supplementary Table 1 . Figure 1 illustrates this breakdown for sex, which is the demographic by which all our following results are stratified due to the imbalance in our sample and the results in Tables 2 and 3 . Social media use and mental health and well-being outcomes are also known to vary according to gender 54 , 55 , 56 .

All social media users in the sample ( N   =  4083) are split by female ( N   =  2698) and male ( N   =  1385), and the frequency with which they use each social media platform given as either ‘Daily’, ‘Less than daily’ or ‘Never’. Labels on the stacked charts give the precise percentage of the group in each of the frequencies for each platform.

Mental health and well-being

First we will consider well-being and indicators of poor mental health across different use frequencies. Figure 2 shows how indicators of poor mental health vary across the three frequencies of use, which are more than 10 times a day, 2–10 times a day and once per day or less; no participants reported using no social media at all. These frequencies are contextualised by the prevalence of each outcome in all users of social media. This figure shows that the lowest category of social media use, that is once per day or less, has the highest proportions of disordered eating, self-harm and suicidal thoughts among women. As seen in Table 2 , only 7.1% of women and 12% of men used social media less than once per day, and so these measurements are subject to wider confidence intervals. Here, depression is defined as being present in those who scored above the cut-off score of 12 in the Short Mood and Feelings Questionnaire (MFQ) 38 . Additional descriptive data about mental health outcomes in the sample is also available in Supplementary Figure 1 and in Supplementary Tables 2 to 6 .

The frequency with which participants used any social media is reported as ‘more than ten times a day’, ‘between two and ten times a day’ or ‘once or less per day’, and the percentage of participants in that group who reported each mental health outcome is given in each sub-plot, with 95% confidence intervals. Disordered eating, self-harm and suicidal thoughts were assessed in the main sample alongside the social media questions ( N   =  4083) and included for those participants who reported them in the past year. Depression ( N   =  2991) was measured in the sub-sample with the Moods and Feelings questionnaire in the year prior to the social media measurement, and uses a cut off of 12 or more to indicate the presence of depression.

Similarly, each well-being construct is presented in Fig. 3 , and contextualised by the result for all users of social media, regardless of frequency. Separate outcomes are presented for the three sub-scales of the Basic Psychological Needs (BPN) scale and the two sub-scales of the Meaning in Life (MIL) scale. The Life Orientation Test measures optimism, and the Warwick Edinburgh Mental Well-being Scale (WEMWBS) measures overall positive well-being.

Each sub-graph presents each of the seven well-being measures, including the Basic Psychological Needs scale (BPN) sub-scales autonomy, relatedness and competence, and the Meaning In Life (MIL) scale’s two sub-scales of presence and search. Satisfaction With Life, the Life Orientation Test, the Gratitude Questionnaire, Subjective Happiness Scale and the Warwick Edinburgh Mental Wellbeing Scale (WEMWBS) are also included. The mean of each scale is given for all participants ( N   =  2991) with 95% confidence intervals, split by male and female, and then for each dichotomous category of use-frequency which is one of ‘more than ten times a day’, ‘between two and ten times a day’ or ‘once or less per day’.

Next we consider the characteristics of daily users of each platform. The relative percentage of daily users against other types of users for each platform can be referred to in Fig. 1 , and versions of Figs. 4 and 5 for all users of each platform are given in Supplementary Figures 2 and 3 .

The percentage of daily users of each platform who have reported each symptom is given in each sub-graph, with 95% confidence intervals. Disordered eating, self-harm and suicidal thoughts were assessed in the main sample alongside the social media questions ( N   =  4083) and included for those participants who reported them in the past year. Depression ( N   =  2991) was measured in the sub-sample with the Moods and Feelings questionnaire in the year prior to the social media measurement, and uses a cut off of 12 or more to indicate the presence of depression. Participants can belong to the daily user group of more than one platform.

Each sub-graph presents each of the seven well-being measures, including the Basic Psychological Needs scale (BPN) sub-scales autonomy, relatedness and competence, and the Meaning In Life (MIL) scale’s two sub-scales of presence and search. Satisfaction With Life, the Life Orientation Test, the Gratitude Questionnaire, Subjective Happiness Scale and the Warwick Edinburgh Mental Wellbeing Scale (WEMWBS) are also included. The mean of each scale is given for all daily users of each platform from the sub-sample ( N   =  2991) with 95% confidence intervals, split by male and female.

Finally Fig. 5 gives the mean well-being score across each platform for each of the seven well-being measures.

This study used data from a UK population cohort study to describe the demographics and key mental health and well-being indicators of social media users by their self-reported frequency of using social media and five different platforms used at ages 23 and 24. Overall, we saw that there were differences in demographics and mental states of users across use-patterns and platforms used. In the following sections, we detail and discuss the implications of these findings for future research across the themes of demographics, use-frequency and platform used.

In general, just over half of participants reported using social media 2–10 times per day, with more than ten times per day still being common at 39%, and only approximately one in ten participants using social media once per day or less. The results showed that those who rated their social media use at the highest frequency (more than ten times per day) were more likely to be women, more likely to be White and more likely have parents who worked in manual occupations. However, sex was the only demographic that appeared to have a statistical relationship with frequency of use, based on a Chi-squared test. Davies and colleagues 57 saw similar results from a Welsh population survey of social media use that found there was a difference in social media use across genders, but not by measures of deprivation.

Figure 1 showed that Facebook is, unsurprisingly, the most popular platform both in being used by 97% of the participants and being the most used platform on a daily basis. Instagram and YouTube showed substantial differences in use patterns across male and female users, with approximately double the percentage of women using Instagram daily as men and, conversely, approximately double the percentage of men using YouTube daily as women. Snapchat also saw higher proportions of daily and overall female users, though this difference between sexes was not as dramatic as for Instagram and YouTube. These patterns of use generally agree with the demographics of users on these sites reported for 18–29-year-old US adults by the Pew Research Center 25 , although our sample saw slightly more Twitter users than their estimated 38%, and fewer YouTube users than their estimated 91% (see Table 3 ). This difference in YouTube users may be partly explained by the fact that it is the only platform with a substantially higher proportion of men than women using it (68% of women vs 83% of men), and that men were under represented in our sample overall compared to women. This emphasises the importance of stratifying results by sex.

Previous research into the demographics of UK Twitter users also aligns with our findings that men and people from higher socio-economic backgrounds are more likely to be Twitter users than women 26 , 28 . Here, we also saw that those from ethnic minority groups are more likely to be Twitter users than White participants, though this is limited by the fact that we could not further separate out results for people with different ethnicities due to the variables available. Across our sample, Twitter was the only social media platform that had a noticeably higher proportion of both A Level educated participants and parents in non-manual occupations. Snapchat saw the reverse pattern with a higher proportion of participants who did not have A Level qualifications and a higher proportion of participants whose parents worked in manual occupations.

Overall, the sex differences between all male and female users varied across outcomes. For instance, a higher percentage of women experienced depression, disordered eating and self-harm overall, but the gap in the prevalence of suicidal thoughts between men and women was much smaller. This concurs with evidence from the last UK-wide psychiatric morbidity survey, in that ‘common mental health disorders’ are more prevalent in women than men 58 . When it came to well-being, we saw that women also displayed higher mean levels of well-being across most measures. Exceptions were the Life Orientation Test, which showed men generally had higher levels of optimism, the Subjective Happiness Scale where scores were roughly equivalent, and the WEMWBS where men’s general well-being was slightly higher. These results, apart from the WEMWBS, are consistent with findings on UK-wide well-being at the time of the survey, and that men tend to have higher optimism in general 59 , 60 . Previous research into the WEMWBS has not generally found large sex differences, but there is evidence that in younger samples there are differences that may be explained by socio-economic status 40 , 41 , 61 ; we note that higher attrition of men in our sample was likely to lead to a bias towards men who are more socio-economically privileged, which may explain why they had higher well-being.

The patterns of mental health outcomes by use frequency displayed in Fig. 2 showed some support for the so-called ‘Goldilocks theory’ of social media use that hypothesises a quadratic, rather than linear, stimulus-response relationship between social media use and mental well-being 62 . This would mean that moderate use of social media, rather than very little or excessive use, is best for well-being. However, this pattern did not consistently apply. For instance, there was an inverse relationship between social media use and percentage of women who self-harm, and in men only the group with the highest level of social media use had more severe depressive symptoms. Previous research has found that in young women higher social media use was associated with increased risk of self-harm 63 , which is in contrast to our results. Similarly, research using the Millennium Cohort Study also found an increasing relationship between objectively measured number of hours spent on social media and how many respondents had clinically relevant symptoms of depression 64 , with a greater increase for girls than boys. Our findings roughly concur with those for the boys, but in women we found that those who used social media the least had the highest rates of depression. However, these differences in findings could reflect the difference in the age of participants or the ways that social media was measured differently across studies. Here we were using use-frequency as categorised into three groups which, as we discuss further in our limitations, may be more reflective of the individual’s mental health and relationship with social media than how frequently they use it 65 .

When considering the results by well-being measure in Fig. 3 we saw that subjective happiness and optimism as measured by the Life Orientation Test both appeared relatively consistent across use categories. Relatedness presented the clearest difference across use categories, with relatedness in women being higher for the two most frequent use frequencies. However, perhaps the most notable outcome was the inconsistency between well-being scales which implies that the choice of scale could affect the interpretation of the impact of well-being on social media use. Research into the relationship between social media use and well-being has been said to suffer from what is known as the ‘jingle-jangle’ paradox where the term ‘well-being’ is used as a catch-all for anything from depression rates to life satisfaction 66 , 67 . This conflation of different well-being measures leads to comparisons of different psychological constructs which may interact differently with social media use: this is hypothesised as one of the reasons that researchers find conflicting evidence for this relationship 66 , which our results support. This also adds to the picture of researcher degrees of freedom in choosing how to measure psychological constructs, which has been shown to have a substantial impact on the outcome of analyses of social media and mental health 15 . Subjective well-being is a complex and multi-faceted psychological concept 68 , 69 , and these findings illustrate the importance of recognising that different measures of well-being could imply different relationships between social media and “well-being”.

When considering participant outcomes by daily users of each platform more consistent patterns emerge than for use-frequencies. We saw that, particularly for women, YouTube had the highest proportion of users reporting disordered eating, self-harm, suicidal thoughts and depression, with higher prevalence of depression in female users of YouTube compared to male users (Fig. 4 ). Whilst overall mental well-being across platforms, as measured by the WEMWBS in Fig. 5 , shows YouTube as being marginally but not drastically lower than other platforms, other well-being measures illustrated some key differences. For instance, YouTube users had lower life satisfaction, relatedness and, particularly for female users, levels of competence (Fig. 5 ). Conversely, daily users of Instagram, and in some cases Snapchat, appeared to have the highest subjective well-being across most measures, with this being particularly noticeable for relatedness, gratitude and happiness (Fig. 5 ). The role of self-determination theory in social media use has previously been explored for Facebook and social media in general 70 with relatedness hypothesised as a key motivating factor for social media use. Previous findings have shown that Instagram and Snapchat are used more for social interaction than Twitter and Facebook 71 , and so our results may corroborate the importance of relatedness in the use of particular platforms. Regardless of the specific measure, our results have illustrated that there is variation amongst platforms which further challenges the idea that ‘social media’ or ‘social networking sites’ are a homogeneous group, and reiterates the importance of understanding the context of research about or using social media 28 , 71 .

At face value, our results appear to directly contrast with the outcomes of the Status of Mind report published by the Royal Society for Public Health 72 , where young people rated YouTube as being the most beneficial site for their well-being and Instagram as the worst, based on health-related outcomes such as their anxiety and depression. Our findings that a higher prevalence of YouTube users suffer from poorer mental health and well-being may mean that whilst some platforms are seen as ‘worse’ for young people’s mental health, that does not equate to finding more unwell young people on those platforms. One explanation may be that those experiencing poorer mental health are more likely to use YouTube because they experience more benefits to their mental health from YouTube, such as community building and peer support 13 , than they do from spending time on sites like Instagram. However, this is certainly an interesting area for further exploration in future quantitative and qualitative research.

Whilst this research draws evidence from a robust and well-documented study and the sample being from a birth cohort means that our results are not confounded by age, there are limitations to the cohort sample that we have used. Firstly, the cohort measures a specific age group so we can only infer information about a single age group at each measurement time point. We suspect that different patterns might be found at different ages, knowing that rates of various mental health conditions such as anxiety, depression and suicidality change over the course of childhood, adolescence and adulthood 73 , and since each generation may use social media differently 74 . It is also important to note that the two data collection points used in this study were taken a year apart, and so not all measures were taken exactly at the same time. This means that although we have primarily considered the data cross-sectionally there is a potential for some longitudinal effects to have influenced the data. Secondly, as discussed in the ‘Methods’ section, there was also a limitation in that ethnicity was only available as two categories (White or Ethnic Minority Groups) and so it was not possible to look further into differences in social media by users of difference ethnicities. Additionally, the make up of the area of Bristol that ALSPAC represents is predominantly White. Given these limitations of the sample it would be valuable to conduct similar research in other cohorts that represent more diverse areas. Thirdly, ALSPAC has seen differential attrition over time and so, as seen in Table 1 , the sample for this study when the index cohort were in their early twenties has fewer men than women, and more participants from privileged socio-economic groups in terms of education and class background 31 . As well as this, typical social media use changes over time and by age 25 , and so further assessment of social media use across a variety of population-representative age groups would be the most effective way to understand differences between generations.

Another limitation of this study is a lack of specificity about the nature of social media use that participants are referring to when responding. It is possible that activities related to ‘using’ social media, such as posting content versus passive use, change depending on platform used and that there are individual preferences to account for 54 , 71 , 75 , 76 . For instance, YouTube is distinct from other platforms in this study in that its primary function is passive content consumption as opposed to social networking. Previous research has suggested a reciprocal association between passive social media use and lower subjective well-being 75 , whilst using social media for direct communication has been positively associated with perceived friend support 77 . This may better reflect the uses of platforms like Snapchat. As well as the subjective nature of ‘use’, there are also ongoing concerns about using self-reported measures of use-frequency to measure social media behaviours 78 , 79 , 80 . Emerging evidence is showing that self-reports do not align well with objective measurement due to recall bias and differences in interpreting how to include notifications or fleeting checks of social media 79 , 80 with self-reported smartphone pickups underestimating associations with mental health compared to objective measures of use 65 . It might be that different ways of measuring social media use, such as types of use, are more useful when considering associations with mental health and well-being outcomes 54 . It is worth noting that the use-frequency measures used in this study are distinct from screen-time, and equivalent use-frequency across platforms may have different time implications; someone may spend short amounts of time on Instagram or Snapchat checking notifications, but do so frequently, versus visiting YouTube once in a day but spending several hours watching content. These nuances are challenging to capture, but by reporting on mental health prevalence across the available responses in a cohort study we can add to the growing understanding of how self-reported social media use frequency is related to mental health. Statistical modelling to test the extent of the differences observed between mental health constructs, use-frequencies and platforms would be valuable future research.

In summary, our results amplify the importance of attending to complexity when measuring and analysing social media use and mental health and well-being. It is important to note that our results do not, and cannot, imply that different types of social media use cause poorer or better health outcomes in young people, but they do provide vital contextual information on user groups that can help us better understand the reasons that previous research has found conflicting results. We have provided estimates of seven well-being measures and the prevalence of four key mental health outcomes (depression, disordered eating, suicidal thoughts and self-harm) across the five platforms Facebook, Twitter, Instagram, Snapchat and YouTube, as well as across three use frequencies. Our findings have shown that the demographic and mental health foot-print of each platform is different. Primarily users differ by sex, but when it comes to platforms YouTube is particularly likely to have both male and female users with poorer mental health and well-being across a range of indicators, alongside evidence that daily Instagram users have better overall well-being than daily users of other platforms. Our findings also indicate that relationships between use-frequency and multiple mental health and well-being outcomes are often non-linear, which supports the importance of considering non-linear dose-response relationships between social media and mental health and well-being in future research. Lastly, we saw that the relationship between use-frequencies and well-being changes depending on the measure of well-being used. This means that we cannot conflate different types of well-being, and doing so will likely result in low replicability.

This research has implications for both those who conduct research on the relationship between social media and mental health, and those who study mental health prediction. We must ensure we are considering both platform-specific and outcome-specific effects rather than conflating types of social media use, social media sites and well-being as single entities. Future research should also stratify results by sex since it is unlikely that studies with differently balanced samples will replicate. Our findings on use-frequencies also suggest that we cannot assume linear relationships between social media use and mental health. Our understanding of these methodological issues would be improved by examining profiles of different user age-groups, as well as examining relationships between these variables longitudinally to understand the potential for reciprocal effects. The differences between platforms should be further considered too, as to how different content types and communication modes on different platforms may affect mental health differently.

Data availability

The datasets analysed during the current study are not publicly available as the informed consent obtained from ALSPAC participants does not allow data to be made freely available through any third party maintained public repository. However, data used for this submission can be made available on request to the ALSPAC Executive, with reference to project number B3227. The ALSPAC data management plan describes in detail the policy regarding data sharing, which is through a system of managed open access. Full instructions for applying for data access can be found here: http://www.bristol.ac.uk/alspac/researchers/access/ . The ALSPAC study website contains details of all the data that are available ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ).

Code availability

The code used to produce the results in this study can be found at https://doi.org/10.17605/OSF.IO/RKXM6 .

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Acknowledgements

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ). The data used in this research was specifically funded by the NIHR (1215-20011), the Wellcome Trust (SSCM.RD1809) and the MRC (102215/2/13/2, MR/M006727/1). N.D. is supported by an MRC GW4 BioMed studentship in Data Science and AI (MR/N013794/1). C.M.A.H. is supported by a Philip Leverhulme Prize. N.H., O.S.P.D. and C.M.A.H. will serve as guarantors for the contents of this paper.

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These authors jointly supervised this work: Oliver S. P. Davis, Claire M. A. Haworth.

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Department of Population Health Science, University of Bristol, Bristol, UK

Nina H. Di Cara, Lizzy Winstone & Oliver S. P. Davis

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

Nina H. Di Cara & Oliver S. P. Davis

Cardiff University, Cardiff, Wales, UK

The Alan Turing Institute, London, UK

Oliver S. P. Davis & Claire M. A. Haworth

Department of Psychological Science, University of Bristol, Bristol, UK

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N.D. was responsible for data curation, formal analysis, investigation, methodology, visualisation and writing (original draft and reviewing and editing). L.W. was responsible for methodology, investigation and writing (reviewing and editing). L.S. was responsible for methodology, investigation, supervision and writing (reviewing and editing). O.D. and C.H. were responsible for funding acquisition, conceptualisation, methodology, investigation, supervision and writing (reviewing and editing).

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Di Cara, N.H., Winstone, L., Sloan, L. et al. The mental health and well-being profile of young adults using social media. npj Mental Health Res 1 , 11 (2022). https://doi.org/10.1038/s44184-022-00011-w

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ORIGINAL RESEARCH article

Effects of social media use on psychological well-being: a mediated model.

• 1 School of Finance and Economics, Jiangsu University, Zhenjiang, China
• 2 Research Unit of Governance, Competitiveness, and Public Policies (GOVCOPP), Center for Economics and Finance (cef.up), School of Economics and Management, University of Porto, Porto, Portugal
• 3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan
• 4 CETYS Universidad, Tijuana, Mexico
• 5 Department of Business Administration, Al-Quds University, Jerusalem, Israel
• 6 Business School, Shandong University, Weihai, China

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Introduction

The use of social media has grown substantially in recent years ( Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” ( Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media ( Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction ( Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction ( Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction ( Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out ( Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others ( Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers ( Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities ( Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel ( Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas ( Carlson et al., 2016 ), which consequently may be significantly correlated to social support ( Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage ( Karikari et al., 2017 ), particularly regarding its societal implications ( Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts ( Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam (1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen (2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam (1995 , 2000) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties ( Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being ( Bano et al., 2019 ). Indeed, Williams (2006) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital ( Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen (2014) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions ( Chen and Li, 2017 ). Abbas and Mesch (2018) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. (2017) also found positive effects of social media use on social capital. Similarly, Pang (2018) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. (2019) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim (2017) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being ( Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities ( Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” ( Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity ( Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities ( Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation ( Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation ( Whaite et al., 2018 ).

Chappell and Badger (1989) stated that social isolation leads to decreased psychological well-being, while Choi and Noh (2019) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. (2012) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” ( Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices ( Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction ( Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones ( Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction ( Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart ( Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities ( Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others ( Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” ( Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing ( Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing ( Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. (2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity ( Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being ( Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

Figure 1 . Conceptual model.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context ( Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones ( Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents ( Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research ( Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data ( Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) ( Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

Table 1 . Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. (2017) . Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan (2015) . Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh (2019) . Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban (2013) . Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas (2018) . Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. (2017) . Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields ( Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” ( Sarstedt and Cheah, 2019 ). According to Ringle et al. (2015) , this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah (2019) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. (2019) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data ( Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 ( Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske (1959) , who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey ( Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings ( Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results ( Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB ( Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold ( Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB ( Hair et al., 2019 ). Hair et al. (2019) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. (1991) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 ( Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

Table 2 . Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability ( Hair et al., 2012 ). Hair et al. (2017) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. (2019) . According to Nunnally (1978) , Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi (1988) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker (1981) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker (1981) , the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

Table 3 . Study measures, factor loading, and the constructs' reliability and convergent validity.

Table 4 . Discriminant validity and correlation.

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. (2019) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power ( Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen (1998) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's (1998) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

Table 5 . Summary of path coefficients and hypothesis testing.

Figure 2 . Structural model.

Table 6 . Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results ( Ringle et al., 2012 ). Hair et al. (2019) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively ( Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. (2019) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit ( Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's (2008) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes (2008) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. (2018) , if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes (2008) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan (2015) and Ellison et al. (2007) , who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. (2021) , who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan (2015) and Karikari et al. (2017) . Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. (2019) , who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li (2017) .

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation ( Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar (2020) . The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh (2019) , social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. (2016) , who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. (2016) , Salehan and Negahban (2013) , and Swar and Hameed (2017) . The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. (2019) , who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat (2019) , who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee (2020) , Chotpitayasunondh and Douglas (2016) , Guazzini et al. (2019) , and Tonacci et al. (2019) , who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas (2018) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. (2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression ( Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. (2018) , who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim (2017) , who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections ( Putnam, 1995 , 2000 ) with heterogeneous weak ties ( Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties ( Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This study is supported by the National Statistics Research Project of China (2016LY96).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Citation: Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM and Liu F (2021) Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Front. Psychol. 12:678766. doi: 10.3389/fpsyg.2021.678766

Received: 10 March 2021; Accepted: 25 May 2021; Published: 21 June 2021.

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Copyright © 2021 Ostic, Qalati, Barbosa, Shah, Galvan Vela, Herzallah and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sikandar Ali Qalati, sidqalati@gmail.com ; 5103180243@stmail.ujs.edu.cn ; Esthela Galvan Vela, esthela.galvan@cetys.mx

† ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342 Sikandar Ali Qalati orcid.org/0000-0001-7235-6098 Belem Barbosa orcid.org/0000-0002-4057-360X Esthela Galvan Vela orcid.org/0000-0002-8778-3989 Feng Liu orcid.org/0000-0001-9367-049X

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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.

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, . "The politics of social media research: We shouldn’t let Meta spin the studies it sponsors." Nieman Journalism Lab . Nieman Foundation for Journalism at Harvard, 8 Jul. 2024. Web. 15 Jul. 2024.

, . (2024, Jul. 8). The politics of social media research: We shouldn’t let Meta spin the studies it sponsors. Nieman Journalism Lab . Retrieved July 15, 2024, from https://www.niemanlab.org/reading/the-politics-of-social-media-research-we-shouldnt-let-meta-spin-the-studies-it-sponsors/

, . "The politics of social media research: We shouldn’t let Meta spin the studies it sponsors." Nieman Journalism Lab . Last modified July 8, 2024. Accessed July 15, 2024. https://www.niemanlab.org/reading/the-politics-of-social-media-research-we-shouldnt-let-meta-spin-the-studies-it-sponsors/.

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Quantitative vs. Qualitative Research Design: Understanding the Differences

As a future professional in the social and education landscape, research design is one of the most critical strategies that you will master to identify challenges, ask questions and form data-driven solutions to address problems specific to your industry. 

Many approaches to research design exist, and not all work in every circumstance. While all data-focused research methods are valid in their own right, certain research design methods are more appropriate for specific study objectives.

Unlock our resource to learn more about jump starting a career in research design — Research Design and Data Analysis for the Social Good .

We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study. 

Understanding Social Phenomena: Qualitative Research Design

Qualitative research focuses on understanding a phenomenon based on human experience and individual perception. It is a non-numerical methodology relying on interpreting a process or result. Qualitative research also paves the way for uncovering other hypotheses related to social phenomena. 

In its most basic form, qualitative research is exploratory in nature and seeks to understand the subjective experience of individuals based on social reality.

Qualitative data is…

• often used in fields related to education, sociology and anthropology; 
• designed to arrive at conclusions regarding social phenomena; 
• focused on data-gathering techniques like interviews, focus groups or case studies; 
• dedicated to perpetuating a flexible, adaptive approach to data gathering;
• known to lead professionals to deeper insights within the overall research study.

You want to use qualitative data research design if:

• you work in a field concerned with enhancing humankind through the lens of social change;
• your research focuses on understanding complex social trends and individual perceptions of those trends;
• you have interests related to human development and interpersonal relationships.

Examples of Qualitative Research Design in Education

Here are just a few examples of how qualitative research design methods can impact education:

Example 1: Former educators participate in in-depth interviews to help determine why a specific school is experiencing a higher-than-average turnover rate compared to other schools in the region. These interviews help determine the types of resources that will make a difference in teacher retention. 

Example 2: Focus group discussions occur to understand the challenges that neurodivergent students experience in the classroom daily. These discussions prepare administrators, staff, teachers and parents to understand the kinds of support that will augment and improve student outcomes.

Example 3: Case studies examine the impacts of a new education policy that limits the number of teacher aids required in a special needs classroom. These findings help policymakers determine whether the new policy affects the learning outcomes of a particular class of students.

Interpreting the Numbers: Quantitative Research Design

Quantitative research tests hypotheses and measures connections between variables. It relies on insights derived from numbers — countable, measurable and statistically sound data. Quantitative research is a strategic research design used when basing critical decisions on statistical conclusions and quantifiable data.

Quantitative research provides numerical-backed quantifiable data that may approve or discount a theory or hypothesis.

Quantitative data is…

• often used in fields related to education, data analysis and healthcare; 
• designed to arrive at numerical, statistical conclusions based on objective facts;
• focused on data-gathering techniques like experiments, surveys or observations;
• dedicated to using mathematical principles to arrive at conclusions;
• known to lead professionals to indisputable observations within the overall research study.

You want to use quantitative data research design if:

• you work in a field concerned with analyzing data to inform decisions;
• your research focuses on studying relationships between variables to form data-driven conclusions;
• you have interests related to mathematics, statistical analysis and data science.

Examples of Quantitative Research Design in Education

Here are just a few examples of how quantitative research design methods may impact education:

Example 1: Researchers compile data to understand the connection between class sizes and standardized test scores. Researchers can determine if and what the relationship is between smaller, intimate class sizes and higher test scores for grade-school children using statistical and data analysis.

Example 2: Professionals conduct an experiment in which a group of high school students must complete a certain number of community service hours before graduation. Researchers compare those students to another group of students who did not complete service hours — using statistical analysis to determine if the requirement increased college acceptance rates.

Example 3: Teachers take a survey to examine an education policy that restricts the number of extracurricular activities offered at a particular academic institution. The findings help better understand the far-reaching impacts of extracurricular opportunities on academic performance.

Making the Most of Research Design Methods for Good: Vanderbilt University’s Peabody College

Vanderbilt University's Peabody College of Education and Human Development offers a variety of respected, nationally-recognized graduate programs designed with future agents of social change in mind. We foster a culture of excellence and compassion and guide you to become the best you can be — both in the classroom and beyond.

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• Child Studies M.Ed. — a rigorous Master of Education degree that prepares students to examine the developmental, learning and social issues concerning children and that allows students to choose from one of two tracks (the Clinical and Developmental Research Track or the Applied Professional Track).
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We invite you to request more information ; we will connect you with an admissions professional who can answer all your questions about choosing one of these transformative graduate degrees at Peabody College. You may also take this opportunity to review our admissions requirements and start your online application today. 

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Effect of social media use on learning, social interactions, and sleep duration among university students ☆

Manjur kolhar.

a Dept. Computer Science, College of Arts and Science, Prince Sattam Bin Abdulaziz University, Wadi Ad Dawser 11990, Saudi Arabia

Raisa Nazir Ahmed Kazi

b College of Applied Medical Science, Prince Sattam Bin Abdulaziz University, Wadi Ad Dawser 11990, Saudi Arabia

c Dept. of Physiology, Al-Ameen Medical College, Bijapur, Karnataka 586108, India

Abdalla Alameen

Associated data.

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Social networking sites are widely used by university students. This study investigated the purposes for which social networking sites are used and their effects on learning, social interaction, and sleep duration.

Material and methods

A cross-sectional study was conducted among 300, 17–29-year-old female students at Prince Sattam bin Abdul Aziz University. A questionnaire was used to collect data. Chi-squared (Fisher’s exact test) test was used to analyze the data.

The results showed that 97% of the students used social media applications. Only 1% of them used social media for academic purposes. Whereas 35% of them used these platforms to chat with others, 43% of them browsed these sites to pass time. Moreover, 57% of them were addicted to social media. Additionally, 52% of them reported that social media use had affected their learning activities, 66% of them felt more drawn toward social media than toward academic activities, and 74% of them spent their free time on social media platforms. The most popular applications (i.e., based on usage) were Snapchat (45%), Instagram (22%), Twitter (18%), and WhatsApp (7%). Further, 46% and 39% of them reported going to bed between 11 pm and 12 am and between 1 am and 2 am, respectively. Finally, 68% of them attributed their delayed bedtime to social media use, and 59% of them reported that social media had affected their social interactions.

Conclusions

A majority of the participants reported prolonged use of social networking sites for nonacademic purposes. These habitual behaviors can distract students from their academic work, adversely affect their academic performance, social interactions, and sleep duration, and lead to a sedentary lifestyle and physical inactivity, which in turn can render them vulnerable to non-communicable diseases and mental health problems.

1. Introduction

Social networking sites and applications are widely used by students. They spend a lot of their time on these sites as a part of their daily lives. Studies revealed that among the various age groups of students, university students are among the most using social networking ( Azizi et al., 2019 ). Social networking sites play a very important role in education. Indeed, students are afforded multiple opportunities to improve learning and access the latest information by connecting with learning groups and other educational systems ( Greenhow and Robelia, 2009 ). Students can also exchange information by connecting with different individuals. This can have a positive impact on student learning outcomes ( Yu et al., 2010 ). Social media also has an impact on student mental health; which refers to their emotional, psychological, and social well-being. University students spend a lot of their time on social media both during the day and at night, and it can be contended that such technologies play an important role in their daily lives. However, despite their tremendous contributions to knowledge acquisition, there is a need to determine whether such technologies are being used to gain knowledge or for other purposes that may lead to the harmful effects of technology misuse.”

Social media has more adverse effects than positive ones ( Woods and Scott, 2016 ). Since students tend to spend more time on social media other than educational purposes; this tends to cause distraction from the learning environment, affecting their academic progress ( Bekalu et al., 2019 , Hettiarachchi, 2014 ). Further, spending a lot of time on social networking sites can lead to a sedentary lifestyle and a decrease in daily physical activity levels, which in turn can render them vulnerable to noncommunicable diseases such as obesity, diabetes, and hypertension ( Melkevik et al., 2015 , Zou et al., 2019 , Hu et al., 2001 ). Additionally, social media use has negative effects on mental health and can lead to depression and anxiety. Therefore, because of the growing numbers of such sites and high demand for social media among university students, it is important to examine the purposes for which social networking sites are used. This study aimed to examine social media use patterns among students. Specifically, we sought to examine the following aspects in this study:

• 1. Duration of time spent on social media platforms during the day and at night
• 2. Purposes for which social media platforms are used and the percentage of students who use social media
• 3. Bedtime, sleep duration, and the time of departure to college
• 4. Effect of social media use on learning and distraction from learning activities
• 5. Effect of social media use on relationships with family members and friends

2. Material and methods

This study was conducted among 300 women, who were students at Prince Sattam bin Abdul Aziz University in Wadi Ad-dawasir. A questionnaire was used to collect data across 4 months (i.e., September to December 2019). The participants provided consent before responding to the survey. This study was conducted among full time students who were willing to participate in the study and honestly answer all the questions. The questions were simple, easy, and translated in Arabic language for a better understanding of the questions. The objective was to obtain accurate information from non-English speaking students. Students who did not respond to the questions appropriately were excluded from the study. Prior to data collection, they were informed about the objectives and methods of the study. The researcher distributed the questionnaire to the students and requested them to read the questions carefully and answer all the questions accurately and honestly. The collected data were kept confidential. The questionnaire assessed the following variables: age, time spent on the internet to use social media (hours), most frequently used social networking site, sleep duration, purposes for which social media platforms were used (academic purposes, chatting, gaming, or movie viewing), time at which college starts, effect of social media use on relationships with family members and friends, social media preoccupation and distraction from academic or learning activities.

2.1. Statistical analyses

Descriptive and inferential statistical analyses were conducted. Continuous variables were examined by computing means, SDs, and ranges, whereas categorical variables were examined by computing frequencies and percentages (%). The significance level was set as 5%. The significance of the difference in categorical variables between two or more groups was examined using the chi-squared test (Fisher’s exact test), which is a nonparametric test for qualitative data analysis. Fisher’s exact test was used when the cell frequencies were very low. Analysis of variance was used to test the significance of the difference in study parameters between three or more groups. SPSS 22.0 and R version 3.2.2 were used for data analysis, and Microsoft Word and Excel were used to generate graphs and tables.

Among 300, a total of 290 students (97%; Fig. 1 ) reported that they used social media applications. Participant ages ranged from 17 to 29 years. Moreover, 30% (n = 90) of them were aged 17–19 years, and 5% (n = 16) of them were aged 25–29 years. A majority of them were aged 20–24 years (65%, n = 194) ( Fig. 2 ).

Percentage of students who reported using social media.

Age distribution of the sample.

With regard to the purposes for which social media platforms were used, only 1% (n = 3, Fig. 3 ) of the students used social media for academic purposes. In contrast, 35% (n = 105, Fig. 3 ) of them used social media to chat with others (i.e., WhatsApp, Facebook, Snapchat), and 43% (n = 129, Fig. 3 ) of them browsed social networking sites to pass time. The other activities in which the students engaged are presented in Fig. 3 .

Purposes for which social media platforms were used by the students.

57% (n = 173) ( Table 1 ) of the students reported that they were addicted to social media. They were more likely to use such technologies to have fun and pass time than for learning purposes. These habits substantially affect academic performance, learning, and knowledge acquisition ( Abbas et al., 2019 ). Moreover, 52% (n = 157) ( Table 1 ) of the students reported that social media use had affected their learning activities significantly (p = 0.035), and 66% (n = 198) ( Table 1 ) of them felt more drawn toward social media than toward studies.

Effect of social media on study time and attraction towards social media than studies.

59% (n-176, Fig. 4 ) of the students reported that excessive social media use had exerted a negative effect on their relationships with their family members and friends and rendered face-to-face communication more challenging. Specifically, 74% (n = 222) ( Table 2 ) of them reported that they spent their free time on social media. In this study, the most widely used application was Snapchat (45%), followed by Instagram (22%), Twitter (18%), and WhatsApp (7%) (p = 0.016*) ( Fig. 7 ). Further, during the day, many students spent more than three hours on social media (57%) Fig. 5 . Similarly, at night, many students spent more than three hours on social media (34%) Fig. 6 .

Percentage of students who reported that social media use had affected their relationships with their family members and friends.

The time spent by students on social media.

Time spent on social media during the day.

Time spent on social media at night.

Most popular social media platforms among the students.

In this study, 46% (n = 139) ( Fig. 10 ) of the students reported going to bed between 11 pm and 12 am, and 39% (n = 118) ( Fig. 10 ) of them reported going to bed between 1 am to 2 am (p = 0.028). Moreover, 93% (n = 279) ( Fig. 9 ) of them left for college at 8 am in the morning, and 68% (n = 205) ( Fig. 8 ) of them attributed their delayed bed time to social media use.

Student perceptions of the effects of social media use on bedtime.

Student responses regarding the time at which their college starts.

Student responses regarding their bedtime.

4. Discussion

4.1. use of social media for academic purposes, addiction, preoccupation with social media use for nonacademic purposes.”, and distraction from learning or academic activities.

Social networking site use is prevalent among university students because of the availability of smartphones and easy access to such sites through home computers. Social media use reduces the amount of time that students spend on academic activities. In this study, only 1% ( Fig. 3 ) of the students used social media for academic purposes, and a majority of them (35–43%) used social media for nonacademic purposes to chat with others (i.e., WhatsApp, Facebook, Snapchat) and browsed social networking sites to pass time Fig. 3 . At present, social media platforms can be used to retrieve necessary information that serves educational purposes. However, social media use negatively affected the academic progress, and studies have shown a strong positive relationship between social media use and academic performance. Most participants used social media platforms to chat rather than for academic purposes. Past studies have found that students who spend more time on social media sites are likely to demonstrate poor academic performance. This is because they spend time chatting online and making friends on social media sites instead of reading books. This has a negative effect on their academic performance ( Owusu-Acheaw and Larson, 2015 , Abbas et al., 2019 ). Therefore, it is important to determine the duration of time that they spend on social media sites and the proportion of time that is spent on social media sites for academic purposes. 57% and 52% of the students reported that they were addicted to social media, and has significantly affected there learning activities (p = 0.035), and 66% of them are more attracted towards social media than studies ( Table 1 ). University students, especially those who feel addicted to social networking sites, access these platforms through their smartphones not only at home but also on campus. Social media plays an important role in education. However, because several social networking sites exist, students spend more time chatting, watching movies, shopping, and playing games rather than on educational activities ( Abbas et al., 2019 ). Because they felt drawn toward new social media platforms, they felt compelled to quickly complete their academic assignments and spend their remaining time playing games or chatting with others through social media platforms. Instead of spending their free time on fun in social media activities, students should use social media platforms for academic purposes or to search for new information and gain more knowledge to improve their academic performance. Failure to do so can have adverse effects on knowledge assimilation and lead to poor performance in competitive examinations. Social media use has increased substantially among university students. Social media use has both positive and negative effects. However, the negative effects are more pronounced because students tend to use such platforms to have fun and pass time rather than for academic purposes. This may distract them from learning and academic activities. This study determined the percentage of students who felt more drawn toward social media than toward academic activities and prioritizing of using social media for fun than academic purposes. The findings underscore the importance of creating awareness about the negative effects of such habits on academic performance among students. This will help students excel in academics and gain adequate knowledge, which in turn will enhance their performance in competitive examinations.

4.2. Effect of social media use (duration of use) on relationships with family members and friends.

In the present study 57% and 34% of the students spend more than three hours on social media during day and night ( Fig. 5 , Fig. 6 ), thus majority of the students spent a total of six hours on social media every day. Although spending a lot of time on one’s mobile phone is not considered to be an abnormal behavior pattern. However, prolonged social media use has mental health effects and young adults are the most vulnerable one.

Studies have shown that social media use is associated with mental disorders, including depression and anxiety ( Hu et al., 2001 ). Although. Social media helps individuals connect with others and develop new relationships. However, such relationships tend to be more formal and transient. Social media users tend to not share close and trusting relationships with their online friends. Moreover, these relationships cannot be compared to the relationships that are developed with friends and family members through face-to-face interactions. 59% of the students reported that excessive time spending on social media has negative impact on their relationship with family and friends. Relying solely on social media (i.e., without physical proximity) to build and maintain relationships can lead to loneliness, alienation, and depression ( Owusu-Acheaw and Larson, 2015 ). Smartphones create a psychological distance between individuals by decreasing face-to-face interactions between family members and friends; and this can negatively affect the quality of time spent on these relationships. This can have a significant effect on social well-being and satisfaction among friends ( Abbas et al., 2019 ). These changes have important behavioral and social implications. Face-to-face interpersonal communication is an important determinant of well-being. Therefore, individuals should spend their free time with their friends and families in person rather than through social media. This may have a more positive impact on mood, enhance psychological satisfaction, and prevent loneliness and depression. 74% (n = 222) ( Table 2 ) of them reported that they spent their free time on social media. In this study, the most widely used application was Snapchat (45%), followed by Instagram (22%), Twitter (18%), and WhatsApp (7%) (p = 0.016*) Figure −7. Further, extensive smartphone use can cause addiction and hamper one’s ability to enjoy his or her free time with family members and friends ( https://www.nationalelfservice.net/mental-health/depression/social-media-good-bad-experiences-impact-depression/ ). In addition, the continuous flow of information through nonstop use of social media can alter sensory perception because constant sensory overload affects learning and memory ( Rotondi et al., 2017 ). Spending one’s free time on social media is not only related to mental health problems but also decreases physical activity levels. This can lead to a sedentary lifestyle and increase one’s risk of developing non-communicable diseases such as diabetes, obesity, and hypertension ( Melkevik et al., 2015 , Zou et al., 2019 , Hu et al., 2001 ).

Among adults, social media use leads to reduced physical activity and increased sitting durations. These changes in turn have a greater impact on the physiological mechanism. This is associated with impaired lipid profiles and glucose uptake, greater energy intake, higher waist circumferences, and greater mortality risk ( Sobaihy, 2017 , Healy et al., 2008b , Healy et al., 2007 ). Social media use increases sitting durations. As a result, sedentary behaviors are commonly observed. Past studies have found that such behaviors lead to increased caloric intake, reduced energy expenditure, and increased adiposity ( Bowman, 2006 ). This leads to the development of the biomarkers associated with cardiometabolic risk factors and an increase in the cardiovascular disease mortality rate. Additionally, weight gain, type 2 diabetes mellitus, some types of cancers, abnormal glucose metabolism, metabolic syndrome, and other cardiovascular risk factors are also associated with physical inactivity among adults. Moreover, it has been reported that there is a progressive increase in mortality rate for each 1-hour increment in sedentary time, and [this] is related to lipoprotein lipase activity ( Howard et al., 2008 , Hu et al., 2003 , Dunstan et al., 2005 , Dunstan et al., 2010 , Jakes et al., 2003 , Healy et al., 2008c , Hamilton et al., 2007 ). Physical inactivity and sedentary behaviors caused by prolonged sitting are associated with decreased skeletal muscle contractility, lipoprotein lipase activity, high-density lipoprotein levels, and reduced glucose uptake. Lipoprotein lipase hydrolyzes plasma triglycerides in lipoproteins and is involved in promoting triglyceride cellular uptake. Reduced plasma lipoprotein lipase leads to decreased peripheral utilization of plasma triglycerides by adipose tissues, skeletal muscle tissues, and lactating mammary glands, which in turn leads to metabolic consequences because of increased plasma triglyceride levels and decreased high-density lipoprotein cholesterol concentrations ( Healy et al., 2008a , Healy et al., 2008c , Hamilton et al., 2007 ). Further, prolonged sitting halts the contractile actions of the large skeletal muscles in the legs, back, and trunk, which are involved in body movement. Thus, physical inactivity leads to low levels of skeletal muscle contraction and decreased calorie spending ( Hamilton et al., 2004 , Bey and Hamilton, 2003 ).

4.3. Effect of social media on sleep duration

According to the American Academy of Sleep Medicine, a minimum of 7 to 9 h of sleep (on a regular basis) is recommended. Sound sleep is associated with improved attention, behavior, learning, memory, emotional regulation, quality of life, and mental and physical health ( Bey and Hamilton, 2003 , Paruthi et al., 2016 ). Sleeping for fewer hours than the recommended duration on a regular basis is associated with attention, behavior, and learning problems. Late-night social media use is prevalent among adults. As a result, they do not get adequate sleep. Past studies have found that sleep disturbances caused by excessive social media use at night adversely affect daytime learning on campus and lead to poor concentration during lectures. Social media use confers many benefits by providing access to a wide range of information sources, which facilitate learning ( Greenhow and Robelia, 2009 ). However, instead of using social networking sites for academic purposes, students tend to be actively involved in online shopping, gaming, and entertainment during the day and at night. These habits distract them from academic activities, minimize their opportunities to gain knowledge, and result in poor academic performance among some students ( Yu et al., 2010 ). Because many students are addicted to social media and use such platforms for nonacademic purposes, it is important to determine the negative effects of social media use. In the present study it was observed that student go to late night sleep, they are deprived of good sleep duration as the college starts at 8 for about 93% of the students, and 68% of the students has reason social media for late night sleep. Sleep deprivation is rapidly becoming prevalent, and it has frequently been linked to late-night use of social networking sites, television viewing, and gaming. Mobile phone use before bedtime is a common habit among many young adults. In this study, 39% to 45% of the students slept for fewer hours than the recommended sleep duration because of late-night social media site use. This can lead to a delayed bedtime, sleep loss, and irregular sleep-wake patterns. Poor sleep quality results in increased tiredness during the day. Sleep has a significant effect on mood, and increasing sleep duration may enhance cognitive performance ( Unhealthy, 2009 ). Sleep restriction may have a negative effect on mood and cognitive function. In addition, social media contents and games may induce pre sleep hyperarousal. Limiting mobile phone use before bedtime may effectively improve sleep by reducing the impact of the light emitted by mobile phones on sleep and reducing the arousal induced by contents browsed on a mobile phone. Past studies have found that restricting mobile phone use at bedtime for four weeks can reduce sleep latency, pre-sleep arousal, and negative affect, increase sleep duration, enhance positive affect, and improve working memory. Sleep is a restorative process that is important for overall health. Sleep deprivation has a negative impact on health, including mental health, and it affects cognitive functioning, motor processes, and emotional stability. Sleep disturbance is also associated with an increased risk of metabolic disturbances such as obesity, hypertension, and diabetes ( Levenson et al., 2016 , Hanson and Huecker, 2019 , Hershner and Chervin, 2014 , Knutson and Van Cauter, 2008 ). Past studies have found that, in both normotensive and hypertensive individuals, sleep deprivation leads to a significant increase in blood pressure and elevated sympathetic nervous system activity ( Gangwisch et al., 2006 , Gangwisch, 2009 ). Elevated sympathetic nervous system activity is related to increased exposure to stress and shorter sleep durations, which in turn can increase salt appetite and suppress renal salt-fluid excretion. This can result in vascular and cardiac complications ( Folkow, 2001 , Bonnet and Arand, 1998 ). Other studies have found that elevated sympathetic nervous system activity associated with sleep disturbance causes glucose intolerance and increases the risk of type 2 diabetes ( Knutson and Van Cauter, 2008 ). Long-term treatment with melatonin (i.e., a night hormone that promotes sleep) can reduce blood pressure in hypertensive individuals ( Gonzalez-Ortiz et al., 2000 , Kawakami et al., 2004 , Scheer et al., 2004 , Beccuti and Pannain, 2011 ). Further, a growing body of empirical evidence yielded by laboratory and epidemiological studies suggests that poor sleep also increases the risk of obesity and associated complications ( Huang et al., 2003 ). Physiologic evidence suggests that short sleep durations contribute to weight gain by influencing appetite, physical activity levels, and thermoregulation. Sleep is an important modulator of neuroendocrine function, and sleep loss can result in endocrine alterations such as increased evening concentrations of cortisol, increased levels of ghrelin, and decreased levels of leptin. A decrease in leptin stimulates appetite and decreases energy expenditure, which in turn can contribute to the development of obesity ( Jean-Louis et al., 2014 ). Past studies have found that there is a strong greater relationship between obesity, insulin resistance, and cardiovascular diseases ( Vorona et al., 2005 , Abbasi et al., 2002 , Scheer et al., 2009 , Knutson et al., 2006 ). When circadian misalignment occurs, this combined effect may serve as a mechanism that underlies an increased risk for obesity, hypertension, and diabetes ( Kohatsu et al., 2006 ). Sleep deprivation has a negative effect on health and predisposes individuals to cardiovascular diseases, obesity, and diabetes at an early age. These habitual factors can be avoided or minimized by creating awareness and disseminating information. Adequate sleep can mitigate the health-related risk factors that are associated with sleep deprivation. The habits that contribute to sleep deprivation should be addressed by conducting awareness programs and implementing coordinated strategies in educational institutions. These efforts should be undertaken by healthcare professionals and academicians as well as within the family.

5. Conclusions

A majority of the students used social networking sites. Excessive social media use for non-academic purpose distracted them from their learning and academic activities and delayed their bed time, which in turn reduced their sleep duration. Further our study reported that, excessive social media use decreases social face to face interaction. This has a negative impact on social well-being and can lead to depression, anxiety, and mood swings. Additionally, late-night social media use reported in the present study can lead to chronic sleep restriction, which plays a significant role in the etiology of diseases associated with metabolic syndrome. Modern lifestyle habits are incompatible with the intrinsic attributes that we inherit. Therefore, interventions should educate individuals about healthier sleep-hygiene practices and help them modify their maladaptive sleep habits. Furthermore, spending a lot of time on social media can increase sitting durations and lower physical activity levels, which in turn can lead to a sedentary lifestyle. This can increase one’s risk of developing metabolic syndrome and chronic non-communicable diseases such as diabetes, hypertension, and obesity.

6. Adverse effects of social media during the coronavirus disease (COVID-19) pandemic

Prolonged social media use for non-academic purposes, addiction of social media, distraction from learning, a lack of sleep, and decreased social interactions were reported by the participants of this study. These findings are more concerning at present because of the ongoing COVID-19 pandemic. Because educational institutions have been closed to curb the spread of COVID-19, colleges and universities have adopted new teaching methods. Traditional teaching methods have been replaced with collaborative multimedia distance learning techniques. Consequently, universities have adopted distance learning strategies.

Traditional teaching methods (i.e., those adopted prior to the COVID-19 outbreak) require students to attend lectures in college. As a result, they spend lesser time on social networking sites, have shorter sitting durations, and engage in some level of physical activity. However, since the outbreak of COVID-19, online learning methods have been adopted. This has prolonged the duration of use of mobile devices and computers, which in turn have increased sitting durations and decreased physical activity levels. These changes may increase one’s risk of developing metabolic syndrome and non-communicable diseases. Additionally, the outbreak of COVID-19 precluded them from engaging in social interactions with their friends in college. This has also could have a negative effect on their mental health and resulting in loneliness and depression. Thus, the COVID-19 pandemic has a major impact on physical activity, face-to-face social interactions, and mental health and resulted in tremendous stress and anxiety. Excessive social media use, caused by the COVID-19 pandemic, could have a negative effects on learning. These changes can adversely affect the psychological health of students. Therefore, communities and families should pay more attention to mental health problems, physical inactivity, and social interactions among students to prevent depression and sedentary lifestyle and lower their risk of developing non-communicable diseases such as obesity, hypertension, and diabetes. These health problems can further strain the medical system, which is already combating a public health emergency. Therefore, to prevent non-communicable diseases and psychosocial stress, individuals should engage in home-based physical activities to ensure that they do not lead a sedentary lifestyle. During this pandemic period, staying active and engaging in routine physical exercise will play an essential role in maintaining mental and physical health. Thus, it is recommended to prevent the COVID-19 pandemic from generating unfavorable mental health issues and cardiovascular consequences due to acute cessation of physical activity.

7. Data availability

Declaration of competing interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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