literature review on impact of social media on students

Use of Social Media in Student Learning and Its Effect on Academic Performance

• First Online: 18 May 2021

Cite this chapter

• G. D. T. D. Chandrasiri 3 &
• S. M. Samarasinghe 3  

Part of the book series: Future of Business and Finance ((FBF))

1050 Accesses

1 Citations

With the advancement of the Internet, social media have become an integral part of our lives, impacting on every aspect of society, and especially in higher education. Thus, understanding the impact of social media on students’ academic performance is inevitable. Social media in higher education has been researched by many, but the impact on students’ academic performance has not been addressed sufficiently, particularly in Sri Lanka. Hence, the objective of this study is to examine the impact of social media on students’ academic performance. A comprehensive model has been formulated and validated using data collected from 320 undergraduates. The measurement model analysis provides adequate construct validity and reliability, and the structural model provides a good model fit. Of the ten hypotheses, nine are supported. The findings reveal that integrating social media in teaching and learning can assist in enhancing students’ academic performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Social Media Impact on Academic Performance: Lessons Learned from Cameroon

Social Media and Higher Education: A Literature Review

Measuring the effect of social media on student academic performance using a social media influence factor model

Acheaw, M. O., & Larson, A. G. (2015). Use of social media and its impact on academic performance of tertiary institution students: A study of students of Koforidua Polytechnic, Ghana. Journal of Education and Practice, 6 , 94–101.

Google Scholar  

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50 (2), 179–211.

Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management system. International Journal of Advanced Computer Science and Application, 5 (1), 143–155.

Article   Google Scholar  

Alnaser, F. M., Ghani, M., Rahi, S., & Abed, H. (2017). The impact of SERVQUAL model and subjective norms on Customer’s satisfaction and customer loyalty in Islamic banks: A cultural context. International Journal of Economics & Management Sciences, 6 (5), 455. https://doi.org/10.4172/2162-6359.1000455 .

Alqasa, K. M., Isa, F. M., Othman, N. S., & Zolait, A. H. S. (2014). The impact of students’ attitude and subjective norm on intention to use of banking systems. International Journal of Business Information Systems, 15 (1), 105–122.

Al-rahmi, W. M., & Othram, M. S. (2013). The Impact of Social Media use on Academic Performance among university students: A Pilot Study. Journal of Information Systems Research and Innovation , 1–10. At https://www.academia.edu/27575207/The_Impact_of_Social_Media_use_on_Academic_Performance_among_university_students_A_Pilot_Study

Al-rahmi, W. M., & Zeki, A. M. (2016). A model of using social media for collaborative learning to enhance learners’ performance on learning. Journal of King Saud University - Computer and Information Sciences, 29 (4), 526–535. https://doi.org/10.1016/j.jksuci.2016.09.002 .

Al-rahmi, W. M., Othman, M. S., & Yusuf, L. M. (2015). The role of social Media for Collaborative Learning to improve academic performance of students and researchers in Malaysian higher education. The International Review of Research in Open and Distributed Learning, 16 (4), 177–204.

Al-rahmi, W. M., Othman, M. S., & Musa, M. A. (2014). The improvement of students’ academic performance by using social media through collaborative learning in Malaysian higher education.  Asian Social Science , 10(8), 210.

Alshuaibi, M. S. I., Alshuaibi, A. S. I., Shamsudin, F. M., & Arshad, D. A. (2018). Use of social media, student engagement, and academic performance of business students in Malaysia. International Journal of Educational Management .

Asur, S., & Huberman, B. A. (2014). Predicting the Future with Social Media, (May). In 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (Vol. 1, pp. 492–499). IEEE. https://doi.org/10.1109/WI-IAT.2010.63 .

Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative science quarterly , 421–458.

Bajpai, P. (2018). Analyzing effect of social media on academic performance of university graduates, (June). https://doi.org/10.1145/3234825.3234830 .

Bandura, A. (1998). Self-efficacy (reprint). In V. S. Ramachaudran (Ed.), Encyclopedia of human behaviour (Vol. 4, 1994) (pp. 71–81). New York: Academic.

Brooks, S. (2015). Computers in human behavior does personal social media usage affect efficiency and Well-being? Computers in Human Behavior, 46 , 26–37. https://doi.org/10.1016/j.chb.2014.12.053 .

Chen, C. (2013). The exploration on network behaviors by using the models of theory of planned behaviors (TPB), technology acceptance model (TAM) and C-TAM-TPB. African Journal of Business Management, 7 (30), 2976–2984. https://doi.org/10.5897/AJBM11.1966 .

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295 (2), 295–336.

Choi, G., & Chung, H. (2013). Applying the technology acceptance model to social networking sites (SNS): Impact of subjective norm and social capital on the acceptance of SNS. International Journal of Human-Computer Interaction, 29 , 619–628. https://doi.org/10.1080/10447318.2012.756333 .

Collins, E., & Hide, B. (2010). Use and relevance of Web 2.0 resources for researchers (pp. 271–289). At https://elpub.architexturez.net/system/files/pdf/119_elpub2010.content.pdf

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

DeLone, W.H., & McLean, E.R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3 (1), 60–95.

Delone, W., & McLean, E. (2003). The Delone and McLean model of information systems success : A ten-year update. Journal of Management Information Systems, 19 (4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 .

Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social media in higher education institutions (HEIs) using the technology acceptance model. International Journal of Educational Technology in Higher Education, 14 (1), 5. https://doi.org/10.1186/s41239-017-0045-2 .

El-Badawy, T. A., & Hashem, Y. (2015). The impact of social media on the academic development of school students. International Journal of Business Administration, 6 (1), 46–52. https://doi.org/10.5430/ijba.v6n1p46 .

Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin, 18 (1), 3–9. https://doi.org/10.1177/0146167292181001 .

Eom, S. B. (2012). Effects of LMS, self-efficacy, and self-regulated learning on LMS effectiveness in business education. Journal of International Education in Business, 5 (2), 129–144. https://doi.org/10.1108/18363261211281744 .

Eom, S. B. (2014). Understanding eLearners’ satisfaction with learning management systems. Bulletin of the IEEE Technical Committee on Learning Technology, 16 (2), 10–13.

Fan, W. S., Haung, Y. K., Hsu, H. C., & Chen, C. C. (2013). An analysis of the Blog-User’ attitude employing structural equation modeling combine TAM and TPB model, 414, 90–93. https://doi.org/10.4028/www.scientific.net/AMM.411-414.90 .

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.

González, M. R., Gasco, J., & Llopis, J. (2016). Facebook and academic performance : A positive outcome. The Anthroplogist, 23 (1-2), 59–67. https://doi.org/10.1080/09720073.2016.11891924 .

Gu, J., Lee, S., & Suh, Y. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36 , 11605–11616. https://doi.org/10.1016/j.eswa.2009.03.024 .

Guo, L., Jian, J., & Tang, C. (2009). Understanding the psychological process underlying customer satisfaction and retention in a relational service. Journal of Business Research, 62 (11), 1152–1159. https://doi.org/10.1016/j.jbusres.2008.10.020 .

Hair, J. F. et al. (2009). Multivariate data analysis . New Delhi: Pearson Education.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19 (2), 139–151. https://doi.org/10.2753/MTP1069-6679190202 .

Hair, J. F., Hult, T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). SAGE Publications.

Henson, R. K. (2001). Understanding internal consistency reliability estimates: A conceptual primer on coefficient alpha. Measurement and evaluation in counseling and development, 34 (3), 177–189.

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W.,... & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational research methods, 17 (2), 182–209.

Hussain, Z. (2016). Leading to intention: The role of attitude in relation to technology acceptance model. Procedia Computer Science , 159–164.

Irianto, H. (2015). Consumers’ attitude and intention towards organic food purchase: An extension of theory of planned behavior in gender perspective. International Journal of Management, Economics and Social Sciences, 4 (1), 17–31.

Isaac, O., Aldholay, A., Abdullah, Z., & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers & Education, 58 (1), 113–129. https://doi.org/10.1016/j.compedu.2019.02.012 .

Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagemnet. Computers and Education, 58 (1), 162–171.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53 (1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.003

Khurana, N. (2015). The impact of social networking sites on the youth. Journal of Mass Communication Journalism, 5 (12), 10–13. https://doi.org/10.4172/2165-7912.1000285 .

Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance . Centre for Learning Sciences and Technologies.

Kolan, B. J. (2018). Effect of social media on academic performance of students in Ghanaian universities: A case study of University of Ghana, Legon (February).

Lau, W. W. F. (2017). Computers in human behavior effects of social media usage and social media multitasking on the academic performance of university students. Computers in Human Behavior, 68 , 286–291. https://doi.org/10.1016/j.chb.2016.11.043 .

Lee, Y. C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30 . https://doi.org/10.1108/14684520610706406 .

Liao, C., Chen, J., & Yen, D. C. (2007). Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior, 23(6), 2804-2822https:// doi.org/10.1016/j.chb.2006.05.006

Lu, Y., Zhou, T., & Wang, B. (2009). Computers in human behavior exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25 (1), 29–39. https://doi.org/10.1016/j.chb.2008.06.002 .

Melani, A., & Andrew, A. (2018). Social media and academic performance of undergraduate students (November 2017).

Michikyan, M., Subrahmanyam, K., & Dennis, J. (2015). Computers in human behavior Facebook use and academic performance among college students: A mixed-methods study with a multi-ethnic sample. Computers in Human Behavior, 45 , 265–272. https://doi.org/10.1016/j.chb.2014.12.033 .

Mingle, J., & Adams, M. (2015). Social media network participation and academic performance in senior high schools in Ghana.  Library Philosophy and Practice , 1.

Moghavvemi, S., Sulaiman, A., & Jaafar, N. I. (2018). The international journal of social media as a complementary learning tool for teaching and learning: The case of youtube. The International Journal of Management Education, 16 (August 2017), 37–42. https://doi.org/10.1016/j.ijme.2017.12.001 .

Nicholas, D., Farm, W., & Rowlands, I. (2014). Social media use in the research workflow (May). https://doi.org/10.3233/ISU-2011-0623 .

Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university student’s behavioral intention to use e-learning. Journal of Educational Technology & Society, 12 (3), 150–162. https://doi.org/10.1007/s00340-009-3513-0 .

Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned Behavio. MIS Quarterly, 30 (1), 115–143.

Pereira, F. A. D. M., Ramos, A. S. M., Gouvêa, M. A., & Da Costa, M. F. (2015). Satisfaction and continuous use intention of e-learning service in Brazilian public organizations. Computers in Human Behavior, 46 , 139–148. https://doi.org/10.1016/j.chb.2015.01.016 .

Phillips, B., & Shipps, B. (2012). Frequency of usage: the impact of technology acceptance factors versus social factors.  International Journal of Virtual Communities and Social Networking (IJVCSN), 4(2), 30-45.

Richter, N. F., Rudolf, S. R., Ringle, C. M., & Schlagel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33 (3), 376–404. https://doi.org/10.1108/IMR-04-2014-0148 .

Rutherford, C. (2010). Using online social media to support preservice student engagement. MERLOT Journal of Online Learning and Teaching, 6 (4), 703–711.

Salloum, S. A., Maqableh, W., Mhamdi, C., & Kurdi, B. Al. (2018). Studying the Social Media Adoption by university students in the United Arab (December).

Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction , 4(4), 295–312, 4, 295–312.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6 (2), 144–176.

Tenopir, C., Volentine, R., & King, D. W. (2013). Social media and scholarly reading. Information Review, online . https://doi.org/10.1108/OIR-04-2012-0062 .

Tess, P. A. (2013). The role of social media in higher education classes (real and virtual)–A literature review. Computers in human behavior, 29 (5), A60–A68.

Thuseethan, S., & Vasanthapriyan, S. (2015). Social media as a new trend in Sri Lankan digital journalism : A surveillance. Asian Social Science, 11 (10), 86. https://doi.org/10.5539/ass.v11n10p86 .

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11 (4), 342–365.

Venkatesh, V., & Davis, F. D. (2000). Studies linked references are available on JSTOR for this article : A theoretical extension of the technology acceptance model : Four longitudinal field studies. Management Science, 46 (2), 186–204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view.  MIS quarterly , 425–478.

Wheeler, S., Yeomans, P., & Wheeler, D. (2008). The good, the bad and the wiki: Evaluating student-generated content for collaborative learning. British Journal of Educational Technology, 39 (6), 987–995. https://doi.org/10.1111/j.1467-8535.2007.00799.x .

Xinli, H. (2013). Effectiveness of information technology in reducing corruption in China. The Electronic Library, 33 (1), 52–64. https://doi.org/10.1108/EL-11-2012-0148 .

Yu, Y., Yi, W., Feng, Y., & Liu, J. (2018). Understanding the intention to use commercial bike-sharing systems: An integration of TAM and TPB . Hawaii International Conference on System Sciences.

Yunus, M. M., & Salehi, H. (2012). The effectiveness of Facebook groups on teaching and improving writing: Students’ perceptions. International journal of education and information Technologies, 1 (6), 87–96.

Download references

Author information

Authors and affiliations.

Department of Information Technology, Faculty of Management Studies and Commerce, University of Sri Jayewardenepura, Nugegoda, Sri Lanka

G. D. T. D. Chandrasiri & S. M. Samarasinghe

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to G. D. T. D. Chandrasiri .

Editor information

Editors and affiliations.

School of Business, Woodbury University, Burbank, CA, USA

Satinder Dhiman

Department of Management, Monash University, Clayton, VIC, Australia

Ramanie Samaratunge

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Chandrasiri, G.D.T.D., Samarasinghe, S.M. (2021). Use of Social Media in Student Learning and Its Effect on Academic Performance. In: Dhiman, S., Samaratunge, R. (eds) New Horizons in Management, Leadership and Sustainability. Future of Business and Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-62171-1_17

Download citation

DOI : https://doi.org/10.1007/978-3-030-62171-1_17

Published : 18 May 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-62170-4

Online ISBN : 978-3-030-62171-1

eBook Packages : Business and Management Business and Management (R0)

Share this chapter

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Review article
• Open access
• Published: 30 January 2021

Understanding students’ behavior in online social networks: a systematic literature review

• Maslin Binti Masrom 1 ,
• Abdelsalam H. Busalim   ORCID: orcid.org/0000-0001-5826-8593 2 ,
• Hassan Abuhassna 3 &
• Nik Hasnaa Nik Mahmood 1  

International Journal of Educational Technology in Higher Education volume  18 , Article number:  6 ( 2021 ) Cite this article

186k Accesses

33 Citations

20 Altmetric

Metrics details

The use of online social networks (OSNs) has increasingly attracted attention from scholars’ in different disciplines. Recently, student behaviors in online social networks have been extensively examined. However, limited efforts have been made to evaluate and systematically review the current research status to provide insights into previous study findings. Accordingly, this study conducted a systematic literature review on student behavior and OSNs to explicate to what extent students behave on these platforms. This study reviewed 104 studies to discuss the research focus and examine trends along with the important theories and research methods utilized. Moreover, the Stimulus-Organism-Response (SOR) model was utilized to classify the factors that influence student behavior. This study’s results demonstrate that the number of studies that address student behaviors on OSNs have recently increased. Moreover, the identified studies focused on five research streams, including academic purpose, cyber victimization, addiction, personality issues, and knowledge sharing behaviors. Most of these studies focused on the use and effect of OSNs on student academic performance. Most importantly, the proposed study framework provides a theoretical basis for further research in this context.

Introduction

The rapid development of Web 2.0 technologies has caused increased usage of online social networking (OSN) sites among individuals. OSNs such as Facebook are used almost every day by millions of users (Brailovskaia et al. 2020 ). OSNs allow individuals to present themselves via virtual communities, interact with their social networks, and maintain connections with others (Brailovskaia et al. 2020 ). Therefore, the use of OSNs has continually attracted young adults, especially students (Kokkinos and Saripanidis 2017 ; Paul et al. 2012 ). Given the popularity of OSNs and the increased number of students of different ages, many education institutions (e.g., universities) have used them to market their educational programs and to communicate with students (Paul et al. 2012 ). The popularity and ubiquity of OSNs have radically changed education systems and motivated students to engage in the educational process (Lambić 2016 ). The children of the twenty-first century are technology-oriented, and thus their learning style differs from previous generations (Moghavvemi et al. 2017a , b ). Students in this era have alternatives to how and where they spend time to learn. OSNs enable students to share knowledge and seek help from other students. Lim and Richardson ( 2016 ) emphasized that one important advantage of OSNs as an educational tool is to increase connections between classmates, which increases information sharing. Furthermore, the use of OSNs has also opened new communication channels between students and teachers. Previous studies have shown that students strengthened connections with their teachers and instructors using OSNs (e.g., Facebook, and Twitter). Therefore, the characteristics and features of OSNs have caused many students to use them as an educational tool, due to the various facilities provided by OSN platforms, which makes learning more fun to experience (Moghavvemi et al. 2017a ). This has caused many educational institutions to consider Facebook as a medium and as a learning tool for students to acquire knowledge (Ainin et al. 2015 ).

OSNs including Facebook, YouTube, and Twitter have been the most utilized platforms for education purposes (Akçayır and Akçayır 2016 ). For instance, the number of daily active users on Facebook reached 1.73 billion in the first quarter of 2020, with an increase of 11% compared to the previous year (Facebook 2020 ). As of the second quarter of 2020, Facebook has over 2.7 billion active monthly users (Clement 2020 ). Lim and Richardson ( 2016 ) empirically showed that students have positive perceptions toward using OSNs as an educational tool. A review of the literature shows that many studies have investigated student behaviors on these sites, which indicates the significance of the current review in providing an in-depth understanding of student behavior on OSNs. To date, various studies have investigated why students use OSNs and explored different student behaviors on these sites. Although there is an increasing amount of literature on this emerging topic, little research has been devoted to consolidating the current knowledge on OSN student behaviors. Moreover, to utilize the power of OSNs in an education context, it is important to study and understand student behaviors in this setting. However, current research that investigates student behaviors in OSNs is rather fragmented. Thus, it is difficult to derive in-depth and meaningful implications from these studies. Therefore, a systematic review of previous studies is needed to synthesize previous findings, identify gaps that need more research, and provide opportunities for further research. To this end, the purpose of this study is to explore the current literature in order to understand student behaviors in online social networks. Accordingly, a systematic review was conducted in order to collect, analyze, and synthesize current studies on student behaviors in OSNs.

This study drew on the Stimulus-Organism-Response (SOR) model to classify factors and develop a framework for better understanding of student behaviors in the context of OSNs. The S-O-R model suggests that various aspects of the environment (S), incite individual cognitive and affective reactions (O), which in turn derives their behavioral responses (R) (Mehrabian and Russell 1974 ). In order to achieve effective results in a clear and understandable manner, five research questions were proposed as shown below.

What was the research regional context covered in previous studies?

What were the focus and trends of previous studies?

What were the research methods used in previous studies?

What were the major theories adopted in previous studies?

What important factors were studied to understand student usage behaviors in OSNs?

This paper is organized as follows. The second section discusses the concept of online social networks and their definition. The third section describes the review method used to extract, analyze, and synthesize studies on student behaviors. The fourth section provides the result of analyzing the 104 identified primary studies and summarizes their findings based on the research questions. The fifth section provides a discussion on the results based on each research question. The sixth section highlights the limitations associated with this study, and the final section provides a conclusion of the study.

• Online social networks

Since online social networks such as Facebook were introduced last decade, they have attracted millions of users and have become integrated into our daily routines. OSNs provide users with virtual spaces where they can find other people with similar interests to communicate with and share their social activities (Lambić et al. 2016 ). The concept of OSNs is a combination of technology, information, and human interfaces that enable users to create an online community and build a social network of friends (Borrero et al. 2014 ). Kum Tang and Koh ( 2017 ) defined OSNs as “web-based virtual communities where users interact with real-life friends and meet other people with shared interests” . A more detailed and well-cited definition of OSN was introduced by Boyd and Ellison ( 2008 ) who defined OSNs as “web-based services that allow individuals to (1) construct a public or semipublic profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system” . Due to its popularity, many researches have examined the effect of OSNs on different disciplines such as business (Kujur and Singh 2017 ), healthcare (Chung 2014 ; Lin et al. 2016 ; Mano 2014 ), psychology (Pantic 2014 ), and education (Hamid et al.  2016 , 2015 ; Roblyer et al. 2010 ).

The heavy use of OSNs by students has led many studies to examine both positive and negative effects of these sites on students, including the time spent on OSNs usage (Chang and Heo 2014 ; Wohn and Larose 2014 ), engagement in academic activities (Ha et al. 2018 ; Sheeran and Cummings 2018 ), as well as the effect of OSN on students’ academic performance. Lim and Richardson ( 2016 ) stated that the main reasons for students to use OSNs as an educational tool is to increase their interactions and establish connections with classmates. Tower et al. ( 2014 ) found that OSN platforms such as Facebook have the potential to improve student self-efficacy in learning and develop their learning skills to a higher level. Therefore, some education institutions have started to develop their own OSN learning platforms (Tally 2010 ). Mazman and Usluel ( 2010 ) highlighted that using OSNs for educational and instructional contexts is an idea worth developing because students spend a lot of time on these platforms. Yet, the educational activities conducted on OSNs are dependent on the nature of the OSNs used by the students (Benson et al. 2015 ). Moreover, for teaching and learning, instructors have begun using OSNs platforms for several other purposes such as increasing knowledge exchanges and effective learning (Romero-Hall 2017 ). On the other hand, previous studies have raised some challenges of using OSNs for educational purposes. For example, students tend to use OSNs as a social tool for entraining rather than an educational tool (Baran 2010 ; Gettman and Cortijo 2015 ). Moreover, the active use of OSNs on daily basis may develop students’ negative behavior such as addiction and distraction. In this context, Kitsantas et al. ( 2016 ) found that college students in the United States reported some concerns such as the OSNs usage can turn into addictive behavior, distraction, privacy threats, the negative impact on their emotional health, and the inability to complete the tasks on time. Another challenge of using OSNs as educational tools is gender differences. Kim and Yoo ( 2016 ) found some differences between male and female students concerning the negative impact of OSNs, for example, female students are more conserved about issues related to security, and the difficulty of task/work completion. Furthermore, innovation is a key aspect in the education process (Serdyukov 2017 ), however, using OSNs as an educational tool, students could lose creativity due to the easy access to everything using these platforms (Mirabolghasemi et al. 2016 ).

Review method

This study employed a Systematic Literature Review (SLR) approach in order to answer the research questions. The SLR approach creates a foundation that advances knowledge and facilitates theory development for a specific topic (Webster and Watson 2002 ). Kitchenham and Charters ( 2007 ) defined SLR as a process of identifying, evaluating, and synthesizing all available research that is related to research questions, area of research, or new phenomenon. This study follows Kitchenhand and Charters’ guidelines (Kitchenham 2004 ), which state that the SLR approach involves three main stages: planning the review, conducting the review, and reporting the review results. There are several motivations for carrying out this systematic review. First, to summarize existing knowledge and evidence on research related to OSNs such as the theories, methods, and factors that influence student behaviors on these platforms. Second, to discover the current research focus and trends in this setting. Third, to propose a framework that classifies the factors that influence student behaviors on OSNs using the S-O-R model. The reasons for using S-O-R model in this study are twofold. First, S-O-R is a crucial theoretical framework to understand individuals’ behavior, and it has been extensively used in previous studies on consumer behavior (Wang and Chang 2013 ; Zhang et al. 2014 ; Zhang and Benyoucef 2016 ), and online users’ behavior (Islam et al. 2018 ; Luqman et al. 2017 ). Second, using the S-O-R model can provide a structured manner to understand the effect of the technological features of OSNs as environmental stimuli on individuals’ behavior (Luqman et al. 2017 ). Therefore, the application of the S-O-R model can provide a guide in the OSNs literature to better understand the potential stimulus and organism factors that drive a student’s behavioral responses in the context of OSNs. The SLR was guided by five research questions (see “ Introduction ” section), which provide an in-depth understanding of the research topic. The rationale and motivation beyond considering these questions are stated in Table 1 .

Stage one: Planning

Before conducting any SLR, it is necessary to clarify the goal and the objectives of the review (Kitchenham and Charters 2007 ). After identifying the review objectives and the research questions, in the planning stage, it is important to design the review protocol that will be used to conduct the review (Kitchenham and Charters 2007 ). Using a clear review protocol will help define criteria for selecting the literature source, database, and search keywords. Review protocol reduce research bias and specifies the research method used to perform a systematic review (Kitchenham and Charters 2007 ). Figure  1 shows the review protocol used for this study.

Review protocol

Stage two: Conducting the review

In this stage relevant literature was collected using a two-stage approach, which was followed by the removal of duplicated articles using Mendeley software. Finally, the researchers applied selection criteria to identify the most relevant articles to the current review. The details of each step of this stage are discussed below:

Literature identification and collection

This study used a two-stage approach (Webster and Watson 2002 ) to identify and collect relevant articles for review. In the first stage, this study conducted a systematic search to identify studies that address student behaviors and the use of online social networks using selected academic databases, including the Web of Science, Wiley Online Library ScienceDirect, Scopus, Emerald, and Springer. The choice of these academic databases is consistent with previous SLR studies (Ahmadi et al. 2018 ; Balaid et al. 2016 ; Busalim and Hussin 2016 ). Derived from the structure of this review and the research questions, these online databases were searched by focusing on title, abstract, and keywords. The search in these databases started in May 2019 using the specific keywords of “students’ behavior”, “online social networking”, “social networking sites”, and “Facebook”. This study performed several searches in each database using Boolean logic operators (i.e., AND and OR) to obtain a large number of published studies related to the review topic.

The results from this stage were 164 studies published between 2010 and 2018. In the second stage, important peer-reviewed journals were checked to ensure that all relevant articles were collected. We used the same keywords to search on information systems and education journals such as Computers in Human Behavior, International Journal of Information Management, Computers and Education, and Education and Information Technologies. These journals among the top peer-reviewed journals that publish topics related to students' behavior, education technologies, and OSNs. The result from both stages was 188 studies related to student behaviors in OSN. Table 2 presents the journals with more than two articles published in these areas.

Study selection

Following the identification of these studies, and after deleting duplicated studies, this study examined title, abstract, or the content of each study using three selection criteria: (1) a focus on student behavior; (2) an examination of the context of online social networks; (3) and a qualification as an empirical study. After applying these criteria, a total of 96 studies remained as primary studies for review. We further conducted a forward manual search on a reference list for the identified primary studies, through which an additional 8 studies were identified. A total of 104 studies were collected. As depicted in Fig.  2 , the frequency of published articles related to student behaviors in online social networks has gradually increased since 2010. In this regard, the highest number of articles were published in 2017. We can see that from 2010 to 2012 the number of published articles was relatively low and significant growth in published articles was seen from 2013 to 2017. This increase reveals that studying the behavior of students on different OSN platforms is increasingly attractive to researchers.

Timeline of publication

For further analysis, this study summarized the key topics covered during the review timeline. Figure  3 visualizes the development of OSNs studies over the years. Studies in the first three years (2010–2012) revolved around the use of OSNs by students and the benefits of using these platforms for educational purposes. The studies conducted between 2013 and 2015 mostly focused on the effect of using OSNs on student academic performance and achievement. In addition, in the same period, several studies examined important psychological issues associated with the use of OSNs such as anxiety, stress, and depression. In the years 2016 to 2018, OSNs studies were expanded to include cyber victimization behavior, OSN addiction behavior such as Facebook addiction, and how OSNs provide a collaborative platform that enables students to share information with their colleagues.

Evolution of OSNs studies over the years

Review results

To analyze the identified studies, this study guided its review using four research questions. Using research questions allows the researcher to synthesize findings from previous studies (Chan et al. 2017 ). The following subsection provides a detailed discussion of each of these research questions.

RQ1: What was the research regional context covered in previous studies?

As shown in Fig.  3 , most primary studies were conducted in the United States (n = 37), followed by Asia (n = 21) and Europe (n = 15). Relatively few studies were conducted in Australia, Africa, and the Middle East (n = 6 each), and only five studies were conducted in more than one country. Most of these empirical studies used university or college students to examine and validate the research models. Furthermore, many of these studies examined student behavior by considering Facebook as an online social network (n = 58) and a few studies examined student behavior on Microblogging platforms like Twitter (n = 7). The rest of the studies used multiple online social networks such as Instagram, YouTube, and Moodle (n = 31).

As shown in Fig.  4 , most of the reviewed studies are conducted in the United States (US). Furthermore, these studies considered Facebook as the main OSN platform. However, the focus on examining the usage behavior of Facebook in Western countries, particularly the US, is one of the challenges of Facebook research, because Facebook is used in many countries with 80% of its users are outside of the US (Peters et al. 2015 ).

Distribution of published studies by region

RQ2: What were the focus and trends of previous studies?

The results indicate that the identified primary studies for student behaviors on online social networks covered a wide spectrum of different research contexts. Further examination shows that there are five research streams in the literature.

The first research stream focused on using OSNs for academic purposes. The educational usage of OSNs relies on their purpose of use. OSNs can improve student engagement in a course and provide them with a sense of connection to their colleagues (Lambić 2016 ). However, the use of OSNs by students can affect their education as students can easily shift from using OSNs for educational to entertainment purposes. Thus, many studies under this stream focus on the effect of OSNs use on student academic performance. For instance, Lambić ( 2016 ) examined the effect of frequent Facebook use on the academic performance of university students. The results showed that students using Facebook as an educational tool to facilitate knowledge sharing and discussion positively impacted academic performance. Consistent with this result, Ainin et al. ( 2015 ) found that data from 1165 university students revealed a positive relationship between Facebook use and student academic performance. On the other hand, Paul et al. ( 2012 ) found that time spent on OSNs negative impacted student academic behavior. Moreover, the results statistically highlight that increased student attention spans resulted in increased time spent on OSNs, which eventually results in a negatively effect on academic performance. The results from Karpinski et al. ( 2013 ) showed that the effect of OSNs usage on student academic performance could differ from one country to another.

In summary, previous studies on the relationship between OSN use and academic performance show mixed results. From the reviewed studies, there were disparate results due to a few reasons. For example, recent studies found that multitasking plays an important role in determining the relationship between OSN usage and student academic performance. Karpinski et al. ( 2013 ) found a negative relationship between using social network sites (SNSs) and Grade Point Average (GPA) that was moderated by multitasking. Moreover, results from Junco ( 2015 ), illustrated that besides multitasking, student class rank is another determinant of the relationship between OSN platforms like Facebook and academic performance. The results revealed that senior students spent significantly less time on Facebook while doing schoolwork than freshman and sophomore students.

The second research stream is related to cyber victimization. Studies in this stream focused on negative interactions on OSNs like Facebook, which is the main platform where cyber victimization occurs (Kokkinos and Saripanidis 2017 ). Moreover, most studies in this stream examined the cyberbullying concept on OSNs. Cyberbullying is defined as “any behavior performed through electronic media by individuals or groups of individuals that repeatedly communicates hostile or aggressive messages intended to inflict harm or discomfort on others” (Tokunaga 2010 , p. 278). For instance, Gahagan et al. ( 2016 ) investigated the experiences of college students with cyberbullying on SNSs, and the results showed that 46% of the tested sample witnessed someone who had been bullied through the use of SNSs. Walker et al. ( 2011 ) conducted an exploratory study among undergraduate students to investigate their cyberbullying experiences. The results of the study highlighted that the majority of respondents knew someone who had been bullied on SNSs (Benson et al. 2015 ).

The third research stream focused on student addiction to OSNs use. Recent research has shown that excessive OSN use can lead to addictive behavior among students (Shettar et al. 2017 ). In this stream, Facebook was the main addictive ONS platform that was investigated (Shettar et al. 2017 ; Hong and Chiu 2016 ; Koc and Gulyagci 2013 ). Facebook addiction is defined as an excessive attachment to Facebook that interferes with daily activities and interpersonal relationships (Elphinston and Noller 2011 ). According to Andreassen et al. ( 2012 ), Facebook addiction has six general characteristics including salience, tolerance, mood modification, withdrawal, conflict, and relapse. As university students frequently have high levels of stress due to various commitments, such as assignment deadlines, exams, and high pressure to perform, they tend to use Facebook for mood modification (Brailovskaia and Margraf 2017 ; Brailovskaia et al. 2018 ). On further analysis, it was noticed that Facebook addiction among students was associated with other factors such as loneliness (Shettar et al. 2017 ), personality traits (i.e., openness agreeableness, conscientiousness, emotional stability, and extraversion) (Błachnio et al. 2017 ; Tang et al. 2016 ), and physical activities (Brailovskaia et al. 2018 ). Studies have examined student addiction behavior on different OSNs platforms. For instance, Ndasauka et al. ( 2016 ), empirically examined excessive Twitter use among college students. Kum Tang and Koh ( 2017 ) investigated the prevalence of different addiction behaviors (i.e., food and shopping addiction) and effective disorders among college students. In addition, a study by Chae and Kim (Chae et al. 2017 ) examined psychosocial differences in ONS addiction between female and male students. The results of the study showed that female students had a higher tendency towards OSNs addiction than male students.

The fourth stream of research highlighted in this review focused on student personality issues such as self-disclosure, stress, depression, loneliness, and self-presentation. For instance, Chen ( 2017 ) investigated the antecedents that predict positive student self-disclosure on SNSs. Tandoc et al. ( 2015 ) used social rank theory and Facebook envy to test the depression scale between college students. Skues et al. ( 2012 ) examined the relationship between three traits in the Big Five Traits model (neuroticism, extraversion, and openness) and student Facebook usage. Chang and Heo ( 2014 ) investigated the factors that explain the disclosure of a student’s personal information on Facebook.

The fifth reviewed research stream focused on student knowledge sharing behavior. For instance, Kim et al. ( 2015 ) identified the personal factors (self-efficacy) and environmental factors (strength of social ties and size of social networks) that affect information sharing behavior amongst university students. Eid and Al-Jabri ( 2016 ) examined the effect of various SNS characteristics (file sharing, chatting and online discussion, content creation, and enjoyment and entertainment) on knowledge sharing and student learning performance. Moghavvemi et al. ( 2017a , b ) examined the relationship between enjoyment, perceived status, outcome expectations, perceived benefits, and knowledge sharing behavior between students on Facebook. Figure  5 provides a mind map that shows an overview of the research focus and trends found in previous studies.

Reviewed studies research focus and trends

RQ3: What were the research methods used in previous studies?

As presented in Fig.  6 , previous studies used several research methods to examine student behavior on online social networks. Surveys were the method used most frequently in primary studies to understand the different types of determinants that effect student behaviors on online social networks, followed by the experiment method. Studies used the experiment method to examine the effect of online social networks content and features on student behavior, For example, Corbitt-Hall et al. ( 2016 ) had randomly assigned students to interact with simulated Facebook content that reflected various suicide risk levels. Singh ( 2017 ) used data mining techniques to collect student interaction data from different social networking sites such as Facebook and Twitter to classify student academic activities on these platforms. Studies that investigated student intentions, perceptions, and attitudes towards OSNs used survey data. For instance, Doleck et al. ( 2017 ) distributed an online survey to college students who used Facebook and found that perceived usefulness, attitude, and self-expression were influential factors towards the use of online social networks. Moreover, Ndasauka et al. ( 2016 ) used the survey method to assess the excessive use of Twitter among college students.

Research method distribution

RQ4: What were the major theories adopted in previous studies?

The results from the SLR show that previous studies used several theories to understand student behavior in online social networks. Table 3 depicts the theories used in these studies, with Use and Gratification Theory (UGT) being the most popular theory use to understand students' behaviors (Asiedu and Badu 2018 ; Chang and Heo 2014 ; Cheung et al. 2011 ; Hossain and Veenstra 2013 ). Furthermore, the social influence theory and the Big Five Traits model were applied in at least five studies each. The theoretical insights into student behaviors on online social networks provided by these theories are listed below:

Motivation aspect: since the advent of online social networks, many studies have been conducted to understand what motivates students to use online social networks. Theories such as UGT have been widely used to understand this issue. For example, Hossain and Veenstra ( 2013 ) conducted an empirical study to investigate what drives university students in the United States of America to use Social Networking Sites (SNSs) using the theoretical foundation of UGT. The study found that the geographic or physical displacement of students affects the use and gratification of SNSs. Zheng Wang et al. ( 2012a , b ) explained that students are motivated to use social media by their cognitive, emotional, social, and habitual needs as well as that all four categories significantly drive students to use social media.

Social-related aspect: Social theories such as Social Influence Theory, Social Learning Theory, and Social Capital Theory have also been used in several previous studies. Social Influence Theory determines what individual behaviors or opinions are affected by others. Venkatesh, Morris, Davis, and Davis (2003) defined social influence as “the degree to which an individual perceives that important others believe he or she should use a new system” . Cheung et al. ( 2011 ) applied Social Influence Theory to examine the effect of social influence factors (subjective norms, group norms, and social identity) on intentions to use online social networks. The empirical results from 182 students revealed that only Group Norms had a significant effect on student intentions to use OSNs. Other studies attempted to empirically examine the effect of other social theories. For instance, Liu and Brown ( 2014 ) adapted Social Capital Theory to investigate whether college students' self-disclosure on SNSs directly affected their social capital. Park et al. ( 2014a , b ) investigated the effect of using SNSs on university student learning outcomes using social learning theory.

Behavioral aspect: This study have noticed that the Theory of Planned Behavior (TPB), Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Unified Theory of Acceptance, and Use of Technology (UTAUT) were also utilized as a theoretical foundation in a number of primary studies. These theories have been widely applied in the information systems (IS) field to provide insights into information technology adoption among individuals (Zhang and Benyoucef 2016 ). In the context of online social networks, there were empirical studies that adapted these theories to understand student usage behaviors towards online social networks such as Facebook. For example, Doleck et al. ( 2017 ) applied TAM to investigate college student usage intentions towards SNSs. Chang and Chen ( 2014 ) applied TRA and TPB to investigate why college students share their location on Facebook. In addition, a recent study used UTAUT to examine student perceptions towards using Facebook as an e-learning platform (Moghavvemi et al. 2017a , b ).

RQ5: What important factors were studied to understand student usage behaviors in OSNs?

Throughout the SLR, this study has been able to identify the potential factors that influence student behaviors in online social networks. Furthermore, to synthesize these factors and provide a comprehensive overview, this study proposed a framework based on the Stimulus-Organism-Response (S-O-R) model. The S-O-R model was developed in environmental psychology by Mehrabian and Russell ( 1974 ). According to Mehrabian and Russell ( 1974 ), environmental cues act as stimuli that can affect an individual’s internal cognitive and affective states, which subsequently influences their behavioral responses. To do so, this study extracted all the factors examined in 104 identified primary studies and classified them into three key concepts: stimulus, organism, and response. The details on the important factors of each component are presented below.

Online social networks stimulus

Stimulus factors are triggers that encourage or prompt students to use OSNs. Based on the SLR results, there are three stimulus dimensions: social stimulus, personal stimulus, and OSN characteristics. Social stimuli are cues embedded in the OSN that drive students to use these platforms. As shown in Fig.  7 , this study has identified six social stimulus factors including social support, social presence, social communication, social enhancement, social network size, and strength of social ties. Previous studies found that social aspects are a potential driver of student usage of OSNs. For instance, Kim et al. ( 2011 ) explored the motivation behind college student use of OSNs and found that seeking social support is one of the primary usage triggers. Lim and Richardson ( 2016 ) stated that using OSNs as educational tools will increase interactions and establish connections between students, which will enhance their social presence. Consistent with this, Cheung et al. ( 2011 ) found that social presence and social enhancement both have a positive effect on student use of OSNs. Other studies have tested the effect of other social factors such as social communication (Lee 2015 ), social network size, and strength of social ties (Chang and Heo 2014 ; Kim et al. 2015 ). Personal stimuli are student motivational factors associated with a specific state that affects their behavioral response. As depicted in Table 4 , researchers have tested different personal student needs that stimulate OSN usage. For instance, Zheng Wang et al. ( 2012a , b ) examined the emotional, social, and cognitive needs that drive students to use OSNs. Moghavvemi et al. ( 2017a , b ) empirically showed that students with a hedonic motivation were willing to use Facebook as an e-learning tool.

Classification framework for student behaviors in online social networks

OSN website characteristics are stimuli related to the cues implanted in an OSN website. In the reviewed studies, it was found that the most well studied OSN characteristics are usefulness and ease of use. Ease of use refers to student perceptions on the extent to which OSN are easy to use whereas usefulness refers to the degree that students believed that using OSN was helpful in enhancing their task performance (Arteaga Sánchez et al. 2014 ). Although student behaviors in OSNs have been widely studied, few studies have focused on OSN characteristics that stimulate student behaviors. For example, Eid and Al-Jabri ( 2016 ) examined the effect of OSN characteristics such as chatting, discussion, content creation, and file sharing. The results showed that file sharing, chatting, and discussion had a positive impact on student knowledge sharing behavior. In summary, Table 4 shows the stimulus factors identified in previous studies and their classification.

Online social networks organisms

Organism in this study’s framework refers to student internal evaluations towards using OSNs. There are four types of organism factors that have been highlighted in the literature. These types include personality traits, values, social, and cognitive reactions. Student personality traits influence the use of OSNs (Skues et al. 2012 ). As shown in Table 4 , self-esteem and self-disclosure were the most examined personality traits associated with student OSN behaviors. Self-esteem refers to an individual’s emotional evaluation of their own worth (Chen 2017 ). For example, Wang et al. ( 2012a , b ) examined the effect of the Big Five personality traits on student use of specific OSN features. The results found that students with high self-esteem were more likely to comment on other student profiles. Self-disclosure refers to the process by which individuals share their feelings, thoughts, information, and experiences with others (Dindia 1995 ). Previous studies have examined student self-disclosure in OSNs to explore information disclosure behavior (Chang and Heo 2014 ), location disclosure (Chang and Chen 2014 ), self-disclosure, and mental health (Zhang 2017 ). The second type of organism factors is value. It has been noticed that there are several value related factors that affect student internal organisms in OSNs. As shown in Table 4 , entertainment and enjoyment factors were the most common value examined in previous studies. Enjoyment is one of the potential drivers of student OSN use (Nawi et al. 2017 ). Eid and Al-Jabri ( 2016 ) found that YouTube is the most dominant OSN platform used by students for enjoyment and entertainment. Moreover, enjoyment and entertainment directly affected student learning performance.

Social organism refers to the internal social behavior of students that affect their use of OSNs. Students interact with OSN platforms when they experience positive social reactions. Previous studies have examined some social organism factors including relationship with faculty members, engagement, leisure activities, social skills, and chatting and discussion. The fourth type of organism factors is cognitive reactions. Parboteeah et al. ( 2009 ) defined cognitive reaction as “the mental process that occurs in an individual’s mind when he or she interacts with a stimulus” . The positive or negative cognitive reaction of students influences their responses towards OSNs. Table 5 presents the most common organism reactions that effect student use of OSNs.

Online social networks response

In this study’s framework, response refers to student reactions to OSNs stimuli and organisms. As shown in Table 5 , academic related behavior and negative behavior are the most common student responses towards OSNs. Studying the effect of OSN usage on student academic performance has been the most common research topic (Lambić 2016 ; Paul et al. 2012 ; Wohn and Larose 2014 ). On the other hand, other studies have examined the negative behavior of students during their usage of ONS, mostly towards ONS addiction (Hong and Chiu 2016 ; Shettar et al. 2017 ) or cyberbullying (Chen 2017 ; Gahagan et al. 2016 ). Table 6 summarizes student responses associated with OSNs use in previous studies.

Discussion and implications

The last two decades have witnessed a dramatic growth in the number of online social networks used among the youth generation. Examining student behaviors on OSN platforms has increasingly attracted scholars. However, there has been little effort to summarize and synthesize these findings. In this review study, a systematic literature review was conducted to synthesize previous research on student behaviors in OSNs to consolidate the factors that influence student behaviors into a classification framework using the S-O-R model. A total of 104 journal articles were identified through a rigorous and systematic search procedure. The collected studies from the literature show an increasing interest in the area ever since 2010. In line with the research questions, our analysis offers insightful results of the research landscape in terms of research regional context, studies focus trends, methodological trends, factors, and theories leveraged. Using the S-O-R model, we synthesized the reviewed studies highlighting the different stimuli, organism, and response factors. We synthesize and classify these factors into social stimuli, personal stimuli, and OSN characteristics, organism factors; personality traits, value, social, and cognitive reaction, and response; academic related behavior, negative behavior, and other responses.

Research regional perspective

The first research question focused on research regional context. The review revealed that most of the studies were conducted in the US followed by European countries, with the majority focusing on Facebook. The results show that the large majority of the studies were based on a single country. This indicates a sustainable research gap in examining the multi-cultural factors in multiple countries. As OSN is a common phenomenon across many counties, considering the culture and background differences can play an essential role in understanding students’ behavior on these platforms. For example, Ifinedo ( 2016 ) collected data from four countries in America (i.e., USA, Canada, Argentina, and Mexico) to understand students’ pervasive adoption of SNSs. The results from the study revealed that the individualism–collectivism culture factor has a positive impact on students' pervasive adoption behavior of SNSs, and the result reported high level of engagement from students who have more individualistic cultures. In the same manner, Kim et al. ( 2011 ) found some cultural differences in use of the SNSs platforms between Korean and US students. For example, considering the social nature of SNSs, the study found that Korean students rely more on online social relationships to obtain social support, where US students use SNSs to seek entertainment. Furthermore, Karpinski et al. ( 2013 ) empirically found significant differences between US and European students in terms of the moderating effect of multitasking on the relationship between SNS use and academic achievement of students. The confirms that culture issues may vary from one country to another, which consequently effect students’ behavior to use OSNs (Kim et al. 2011 ).

Studies focus and trends

The second research question of this review focused on undersigning the topics and trends that have been discussed in extant studies. The review revealed evidence of five categories of research streams based on the research focus and trend. As shown in Fig.  5 , most of the reviewed studies are in the first stream, which is using OSNs for academic purposes. Moreover, the trend of these studies in this stream focus on examining the effect of using OSNs on students’ academic performance and investigating the use of OSNs for educational purposes. However, a number of other trends are noteworthy. First, as cyber victimization is a relatively new concept, most of the studies provide rigorous effort in exporting the concept, and the reasons beyond its existence among students; however, we have noticed that no effort has been made to investigate the consequences of this negative behavior on students’ academic performance, social life, and communication. Second, we identified only two studies that examined the differences between undergraduate and postgraduate students in terms of cyber victimization. Therefore, there are many avenues for further research to untangle the demographic, education level, and cultural differences in this context. Third, our analysis revealed that Facebook was the most studied ONS platform in terms of addiction behavior, however, over the last ten years, the rapid growth of using image-based ONS such as Instagram and Pinterest has attracted many students (Alhabash and Ma 2017 ). For example, Instagram represents the fastest growing OSNs among young adult users age between 18 and 29 years old (Alhabash and Ma 2017 ). The overwhelming majority of the studies focus on Facebook users, and very few studies have examined excessive Instagram use (Kırcaburun and Griffiths 2018 ; Ponnusamy et al. 2020 ). Although OSNs have many similar features, each platform has unique features and a different structure. These differences in OSNs platforms urge further research to investigate and understand the factors related to excessive and addiction use by students (Kircaburun and Griffiths 2018 ). Therefore, based on the current research gaps, future research agenda including three topics/trend need to be considered. We have developed research questions for each topic as a direction for any further research as shown in Table 7 .

Theories and research methods

The third and fourth research questions focused on understanding the trends in terms of research methods and theories leveraged in existing studies. In relation to the third research question, the review highlighted evidence of the four research methods (i.e., survey, experiment, focus group/interview, and mix method) with a heavy focus on using a quantitative method with the majority of studies conducting survey. This may call for utilizing a variety of other research methods and research design to have more in-depth understanding of students’ behavior on OSN. For example, we noticed that few studies leveraged qualitative methods such as interviews and focus groups (n = 5). In addition, using mix method may derive more results and answer research questions that other methods cannot answer (Tashakkori and Teddlie 2003 ). Experimental methods have been used sparingly (n = 10), this may trigger an opportunity for more experimental research to test different strategies that can be used by education institutions to leverage the potential of OSN platforms in the education process. Moreover, considering that students’ attitude and behavior will change over time, applying longitudinal research method may offer opportunities to explore students’ attitude and behavior patterns over time.

The fourth research question focused on understanding the theoretical underpinnings of the reviewed studies. The analysis revealed two important insights; (1) a substantial number of the reviewed studies do not explicitly use an applied theory, and (2) out of the 34 studies that used theory, nine studies applied UGT to understand the motivation beyond using the OSN. Our findings categorized these theories under three aspects; motivational, social, and behavioral. While each aspect and theory offers useful lenses in this context, there is a lack of leveraging other theories in the extant literature. This motivates researchers to underpin their studies in theories that provide more insights into these three aspects. For example, majority of the studies have applied UGT to understand students’ motivate for using OSNs. However, using other motivational theories could uncover different factors that influence students' motivation for using OSNs. For example, self-determination theory (SDT) focuses on the extent to which individual’s behavior is self-motivated and determined. According to Ryan and Deci ( 2000 ), magnitude and types both shape individuals’ extrinsic motivation. The extrinsic motivation is a spectrum and depends on the level of self-determination (Wang et al. 2019 ). Therefore, the continuum aspect proposed by SDT can provide in-depth understanding of the extrinsic motivation. Wang et al. ( 2016 ) suggested that applying SDT can play a key role in understanding SNSs user satisfaction.

Another theoretical perspective that is worth further exploration relates to the psychological aspect. Our review results highlighted that a considerable number of studies focused on an important issue arising from the daily use of OSNs, such as excessive use/addiction (Koc and Gulyagci 2013 ; Shettar et al. 2017 ), Previous studies have investigated the behavior aspect beyond these issues, however, understanding the psychological aspect of Facebook addiction is worth further investigation. Ryan et al. ( 2014 ) reviewed Facebook addiction related studies and found that Facebook addiction is also linked to psychological factors such as depression and anxiety.

Factors that influence students behavior: S-O-R Framework

The fifth research question focused on determining the factors studies in the extant literature. The review analysis showed that stimuli factors included social, personal, and OSNs website stimuli. However, different types of stimuli have received less attention than other stimuli. Most studies leveraged the social and students’ personal stimuli. Furthermore, few studies conceptualized the OSNs websites characterises in terms of students beliefs about the effect of OSNs features and functions (e.g., perceived ease of use, user friendly) on students stimuli; it would be significant to develop a typology of the OSNs websites stimuli and systematically examine their effect on students’ attitude and behavior. We recommend applying different theories (as mentioned in Theories and research methods section) as an initial step to further identify stimuli factors. The results also highlight that cognitive reaction plays an essential role in the organism dimension. When students encounter stimuli, their internal evaluation is dominated by emotions. Therefore, the cognitive process takes place between students’ usage behavior and their responses (e.g., effort expectancy). In this review, we reported few studies that examined the effect of the cognitive reaction of students.

Response factors encompass students’ reaction to OSNs platforms stimuli and organism. Our review revealed an unsurprisingly dominant focus on the academic related behavior such as academic performance. While it is important to examine the effect of various stimuli and organism factors on academic related behavior and OSNs negative behavior, the psychological aspect beyond OSNs negative behavior is equallty important.

Limitations

Similar to other systematic review studies, this study has some limitations. The findings of our review are constrained by only empirical studies (journal articles) that meet the inclusion criteria. For instance, we only used the articles that explicitly examined students’ behavior in OSNs. Moreover, other different types of studies such as conference proceedings are not included in our primary studies. Further research efforts can gain additional knowledge and understanding from practitioner articles, books and, white papers. Our findings offer a comprehensive conceptual framework to understand students’ behavior in OSNs; future studies are recommended to perform a quantitative meta-analysis to this framework and test the relative effect of different stimuli factors.

Conclusions

The use of OSNs has become a daily habit among young adults and adolescents these days (Brailovskaia et al. 2020 ). In this review, we used a rigorous systematic review process and identified 104 studies related to students’ behavior in OSNs. We systematically reviewed these studies and provide an overview of the current state of this topic by uncovering the research context, research focus, theories, and research method. More importantly, we proposed a classification framework based on S-O-R model to consolidate the factors that influence students in online social networks. These factors were classified under different dimensions in each category of the S-O-R model; stimuli (Social Stimulus, Personal Stimulus, and OSN Characteristics), organism (Personality traits, value, social, Cognitive reaction), and students’ responses (academic-related behavior, negative behavior, and other responses). This framework provides the researchers with a classification of the factors that have been used in previous studies which can motivate further research on the factors that need more empirical examination (e.g., OSN characteristics).

Availability of data and materials

Not applicable.

Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., & Alizadeh, M. (2018). The application of internet of things in healthcare: A systematic literature review and classification. Universal Access in the Information Society . https://doi.org/10.1007/s10209-018-0618-4 .

Article   Google Scholar  

Ainin, S., Naqshbandi, M. M., Moghavvemi, S., & Jaafar, N. I. (2015). Facebook usage, socialization and academic performance. Computers and Education, 83, 64–73. https://doi.org/10.1016/j.compedu.2014.12.018 .

Akbari, E., Naderi, A., Simons, R.-J., & Pilot, A. (2016). Student engagement and foreign language learning through online social networks. Asian-Pacific Journal of Second and Foreign Language Education, 1 (1), 4. https://doi.org/10.1186/s40862-016-0006-7 .

Akcaoglu, M., & Bowman, N. D. (2016). Using instructor-led Facebook groups to enhance students’ perceptions of course content. Computers in Human Behavior, 65, 582–590. https://doi.org/10.1016/j.chb.2016.05.029 .

Akçayır, G., & Akçayır, M. (2016). Research trends in social network sites’ educational use: A review of publications in all SSCI journals to 2015. Review of Education, 4 (3), 293–319.

Alhabash, S., & Ma, M. (2017). A tale of four platforms: Motivations and uses of Facebook, Twitter, Instagram, and Snapchat among college students. Social Media + Society, 3 (1), 205630511769154. https://doi.org/10.1177/2056305117691544 .

Amador, P., & Amador, J. (2014). Academic advising via Facebook: Examining student help seeking. Internet and Higher Education, 21, 9–16. https://doi.org/10.1016/j.iheduc.2013.10.003 .

Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110 (2), 501–517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517 .

Arteaga Sánchez, R., Cortijo, V., & Javed, U. (2014). Students’ perceptions of Facebook for academic purposes. Computers and Education, 70, 138–149. https://doi.org/10.1016/j.compedu.2013.08.012 .

Asiedu, N. K., & Badu, E. E. (2018). Motivating issues affecting students’ use of social media sites in Ghanaian tertiary institutions. Library Hi Tech, 36 (1), 167–179. https://doi.org/10.1108/LHT-10-2016-0108 .

Asterhan, C. S. C., & Bouton, E. (2017). Teenage peer-to-peer knowledge sharing through social network sites in secondary schools. Computers & Education, 110, 16–34. https://doi.org/10.1016/j.compedu.2017.03.007 .

Balaid, A., Abd Rozan, M. Z., Hikmi, S. N., & Memon, J. (2016). Knowledge maps: A systematic literature review and directions for future research. International Journal of Information Management, 36 (3), 451–475.

Baran, B. (2010). Facebook as a formal instructional environment. British Journal of Educational Technology, 41 (6), 146–149. https://doi.org/10.1111/j.1467-8535.2010.01115.x .

Benson, V., & Filippaios, F. (2015). Collaborative competencies in professional social networking: Are students short changed by curriculum in business education? Computers in Human Behavior, 51, 1331–1339. https://doi.org/10.1016/j.chb.2014.11.031 .

Benson, V., Saridakis, G., & Tennakoon, H. (2015). Purpose of social networking use and victimisation: Are there any differences between university students and those not in HE? Computers in Human Behavior, 51, 867–872. https://doi.org/10.1016/j.chb.2014.11.034 .

Błachnio, A., Przepiorka, A., Senol-Durak, E., Durak, M., & Sherstyuk, L. (2017). The role of personality traits in Facebook and Internet addictions: A study on Polish, Turkish, and Ukrainian samples. Computers in Human Behavior, 68, 269–275. https://doi.org/10.1016/j.chb.2016.11.037 .

Borrero, D. J., Yousafzai, Y. S., Javed, U., & Page, L. K. (2014). Perceived value of social networking sites (SNS) in students’ expressive participation in social movements. Journal of Research in Interactive Marketing, 8 (1), 56–78. https://doi.org/10.1108/JRIM-03-2013-0015 .

Borrero, J. D., Yousafzai, S. Y., Javed, U., & Page, K. L. (2014). Expressive participation in Internet social movements: Testing the moderating effect of technology readiness and sex on student SNS use. Computers in Human Behavior, 30, 39–49. https://doi.org/10.1016/j.chb.2013.07.032 .

Boyd, D. M., & Ellison, N. B. (2008). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13 (1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x .

Brailovskaia, J., & Margraf, J. (2017). Facebook Addiction Disorder (FAD) among German students—A longitudinal approach. PLoS ONE, 12 (12), 1–15. https://doi.org/10.1371/journal.pone.0189719 .

Brailovskaia, J., Ströse, F., Schillack, H., & Margraf, J. (2020). Less Facebook use—More well-being and a healthier lifestyle? An experimental intervention study. Computers in Human Behavior, 108 (March), 106332. https://doi.org/10.1016/j.chb.2020.106332 .

Brailovskaia, J., Teismann, T., & Teismann, T. (2018). Physical activity mediates the association between daily stress and Facebook Addiction Disorder. Computers in Human Behavior, 86, 199–204. https://doi.org/10.1016/j.chb.2018.04.045 .

Busalim, A. H., & Hussin, A. R. C. (2016). Understanding social commerce: A systematic literature review and directions for further research. International Journal of Information Management, 36 (6), 1075–1088. https://doi.org/10.1016/j.ijinfomgt.2016.06.005 .

Cain, J., Scott, D. R., Tiemeier, A. M., Akers, P., & Metzger, A. H. (2013). Social media use by pharmacy faculty: Student friending, e-professionalism, and professional use. Currents in Pharmacy Teaching and Learning, 5 (1), 2–8. https://doi.org/10.1016/j.cptl.2012.09.002 .

Chae, D., Kim, H., & Kim, Y. A. (2017). Sex differences in the factors influencing Korean College Students’ addictive tendency toward social networking sites. International Journal of Mental Health and Addiction . https://doi.org/10.1007/s11469-017-9778-3 .

Chan, T. K. H., Cheung, C. M. K., & Lee, Z. W. Y. (2017). The state of online impulse-buying research: A literature analysis. Information & Management, 54 (2), 204–217. https://doi.org/10.1016/j.im.2016.06.001 .

Chang, C. W., & Chen, G. M. (2014). College students’ disclosure of location-related information on Facebook. Computers in Human Behavior, 35, 33–38. https://doi.org/10.1016/j.chb.2014.02.028 .

Chang, C. W., & Heo, J. (2014). Visiting theories that predict college students’ self-disclosure on Facebook. Computers in Human Behavior, 30, 79–86. https://doi.org/10.1016/j.chb.2013.07.059 .

Chen, B., & Marcus, J. (2012). Students’ self-presentation on Facebook: An examination of personality and self-construal factors. Computers in Human Behavior, 28 (6), 2091–2099. https://doi.org/10.1016/j.chb.2012.06.013 .

Chen, H. (2017). Antecedents of positive self-disclosure online: An empirical study of US college students Facebook usage. Psychology Research and Behavior Management, 10, 147–153. https://doi.org/10.2147/PRBM.S136049 .

Cheung, C. M. K., Chiu, P. Y., & Lee, M. K. O. (2011). Online social networks: Why do students use facebook? Computers in Human Behavior, 27 (4), 1337–1343.

Chung, J. E. (2014). Social networking in online support groups for health: How online social networking benefits patients. Journal of Health Communication, 19 (6), 639–659. https://doi.org/10.1080/10810730.2012.757396 .

Čičević, S., Samčović, A., & Nešić, M. (2016). Exploring college students’ generational differences in Facebook usage. Computers in Human Behavior, 56, 83–92. https://doi.org/10.1016/j.chb.2015.11.034 .

Clement, J. (2020). Facebook: Number of monthly active users worldwide 2008–2020 Published by J. Clement, Aug 10, 2020 How many users does Facebook have? With over 2.7 billion monthly active users as of the second quarter of 2020, Facebook is the biggest social network world . Retrieved from https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/

Corbitt-Hall, D. J., Gauthier, J. M., Davis, M. T., & Witte, T. K. (2016). College Students’ responses to suicidal content on social networking sites: An examination using a simulated facebook newsfeed. Suicide and Life-Threatening Behavior, 46 (5), 609–624. https://doi.org/10.1111/sltb.12241 .

Deng, L., & Tavares, N. J. (2013). From Moodle to Facebook: Exploring students’ motivation and experiences in online communities. Computers & Education, 68, 167–176. https://doi.org/10.1016/j.compedu.2013.04.028 .

Dindia, K. (1995). Self-disclosure: A sense of balance. Contemporary psychology: A journal of reviews . Vol. 40. New York: Sage Publications. https://doi.org/10.1037/003319 .

Book   Google Scholar  

Doleck, T., Bazelais, P., & Lemay, D. J. (2017). Examining the antecedents of social networking sites use among CEGEP students. Education and Information Technologies, 22 (5), 2103–2123. https://doi.org/10.1007/s10639-016-9535-4 .

Eid, M. I. M., & Al-Jabri, I. M. (2016). Social networking, knowledge sharing, and student learning: The case of university students. Computers and Education, 99, 14–27. https://doi.org/10.1016/j.compedu.2016.04.007 .

Elphinston, R. A., & Noller, P. (2011). Time to Face It! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology, Behavior, and Social Networking, 14 (11), 631–635. https://doi.org/10.1089/cyber.2010.0318 .

Enskat, A., Hunt, S. K., & Hooker, J. F. (2017). A generational examination of instructional Facebook use and the effects on perceived instructor immediacy, credibility and student affective learning. Technology, Pedagogy and Education, 26 (5), 545–557. https://doi.org/10.1080/1475939X.2017.1354065 .

Facebook. (2020). Facebook Reports First Quarter 2020 Results . Retrieved from http://investor.fb.com/releasedetail.cfm?ReleaseID=842071 .

Fasae, J. K., & Adegbilero-Iwari, I. (2016). Use of social media by science students in public universities in Southwest Nigeria. The Electronic Library, 34 (2), 213–222. https://doi.org/10.1108/EL-11-2014-0205 .

Gahagan, K., Vaterlaus, J. M., & Frost, L. R. (2016). College student cyberbullying on social networking sites: Conceptualization, prevalence, and perceived bystander responsibility. Computers in Human Behavior, 55, 1097–1105. https://doi.org/10.1016/j.chb.2015.11.019 .

George, D. R., Dellasega, C., Whitehead, M. M., & Bordon, A. (2013). Facebook-based stress management resources for first-year medical students: A multi-method evaluation. Computers in Human Behavior, 29 (3), 559–562. https://doi.org/10.1016/j.chb.2012.12.008 .

Gettman, H. J., & Cortijo, V. (2015). “Leave Me and My Facebook Alone!” understanding college students’ relationship with Facebook and its use for academic purposes. International Journal for the Scholarship of Teaching and Learning Article, 9 (1), 1. https://doi.org/10.20429/ijsotl.2015.090108 .

Ha, L., Joa, C. Y., Gabay, I., & Kim, K. (2018). Does college students’ social media use affect school e-mail avoidance and campus involvement? Internet Research, 28 (1), 213–231. https://doi.org/10.1108/IntR-11-2016-0346 .

Hamid, S., Bukhari, S., Ravana, S. D., Norman, A. A., & Ijab, M. T. (2016). Role of social media in information-seeking behaviour of international students: A systematic literature review. Aslib Journal of Information Management, 68 (5), 643–666. https://doi.org/10.1108/AJIM-03-2016-0031 .

Hamid, S., Waycott, J., Kurnia, S., & Chang, S. (2015). Understanding students’ perceptions of the benefits of online social networking use for teaching and learning. Internet and Higher Education, 26, 1–9. https://doi.org/10.1016/j.iheduc.2015.02.004 .

Hong, F. Y., & Chiu, S. L. (2016). Factors influencing facebook usage and facebook addictive tendency in university students: The role of online psychological privacy and facebook usage motivation. Stress and Health, 32 (2), 117–127. https://doi.org/10.1002/smi.2585 .

Hossain, M. D., & Veenstra, A. S. (2013). Online maintenance of life domains: Uses of social network sites during graduate education among the US and international students. Computers in Human Behavior, 29 (6), 2697–2702. https://doi.org/10.1016/j.chb.2013.07.007 .

Ifinedo, P. (2016). Applying uses and gratifications theory and social influence processes to understand students’ pervasive adoption of social networking sites: Perspectives from the Americas. International Journal of Information Management, 36 (2), 192–206. https://doi.org/10.1016/j.ijinfomgt.2015.11.007 .

Islam, T., Sheikh, Z., Hameed, Z., Khan, I. U., & Azam, R. I. (2018). Social comparison, materialism, and compulsive buying based on stimulus-response- model: A comparative study among adolescents and young adults. Young Consumers, 19 (1), 19–37. https://doi.org/10.1108/MRR-09-2015-0216 .

Josefsson, P., Hrastinski, S., Pargman, D., & Pargman, T. C. (2016). The student, the private and the professional role: Students’ social media use. Education and Information Technologies, 21 (6), 1583–1594. https://doi.org/10.1007/s10639-015-9403-7 .

Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers and Education, 58 (1), 162–171. https://doi.org/10.1016/j.compedu.2011.08.004 .

Junco, R. (2015). Student class standing, Facebook use, and academic performance. Journal of Applied Developmental Psychology, 36, 18–29. https://doi.org/10.1016/j.appdev.2014.11.001 .

Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., & Ochwo, P. (2013). An exploration of social networking site use, multitasking, and academic performance among United States and European university students. Computers in Human Behavior, 29 (3), 1182–1192. https://doi.org/10.1016/j.chb.2012.10.011 .

Kim, J., Lee, C., & Elias, T. (2015). Factors affecting information sharing in social networking sites amongst university students. Online Information Review, 39 (3), 290–309. https://doi.org/10.1108/OIR-01-2015-0022 .

Kim, S., & Yoo, S. J. (2016). Age and gender differences in social networking: effects on south Korean students in higher education. In Social networking and education (pp. 69–82). Switzerland: Springer. https://doi.org/10.1007/978-3-319-17716-8_5 .

Kim, Y., Sohn, D., & Choi, S. M. (2011). Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Computers in Human Behavior, 27 (1), 365–372. https://doi.org/10.1016/j.chb.2010.08.015 .

Kircaburun, K., & Griffiths, M. D. (2018). Instagram addiction and the Big Five of personality: The mediating role of self-liking. Journal of Behavioral Addictions, 7 (1), 158–170. https://doi.org/10.1556/2006.7.2018.15 .

Kırcaburun, K., & Griffiths, M. D. (2018). Problematic Instagram use: The role of perceived feeling of presence and escapism. International Journal of Mental Health and Addiction . https://doi.org/10.1007/s11469-018-9895-7 .

Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele University, UK and National ICT Australia, 33, 28.

Google Scholar  

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University and University of Durham, 2, 1051.

Kitsantas, A., Dabbagh, N., Chirinos, D. S., & Fake, H. (2016). College students’ perceptions of positive and negative effects of social networking. In Social networking and education (pp. 225–238). Switzerland: Springer, Cham. https://doi.org/10.1007/978-3-319-17716-8_14 .

Koc, M., & Gulyagci, S. (2013). Facebook addiction among Turkish College students: The role of psychological health, demographic, and usage characteristics. Cyberpsychology, Behavior, and Social Networking, 16 (4), 279–284. https://doi.org/10.1089/cyber.2012.0249 .

Kokkinos, C. M., & Saripanidis, I. (2017). A lifestyle exposure perspective of victimization through Facebook among university students. Do individual differences matter? Computers in Human Behavior, 74, 235–245. https://doi.org/10.1016/j.chb.2017.04.036 .

Krasilnikov, A., & Smirnova, A. (2017). Online social adaptation of first-year students and their academic performance. Computers and Education, 113, 327–338. https://doi.org/10.1016/j.compedu.2017.05.012 .

Kujur, F., & Singh, S. (2017). Engaging customers through online participation in social networking sites. Asia Pacific Management Review, 22 (1), 16–24. https://doi.org/10.1016/j.apmrv.2016.10.006 .

Kumar Bhatt, R., & Kumar, A. (2014). Student opinion on the use of social networking tools by libraries. The Electronic Library, 32 (5), 594–602. https://doi.org/10.1108/EL-09-2012-0110 .

Kuo, T., & Tang, H. L. (2014). Relationships among personality traits, Facebook usages, and leisure activities—A case of Taiwanese college students. Computers in Human Behavior, 31 (1), 13–19. https://doi.org/10.1016/j.chb.2013.10.019 .

Lambić, D. (2016). Correlation between Facebook use for educational purposes and academic performance of students. Computers in Human Behavior, 61, 313–320. https://doi.org/10.1016/j.chb.2016.03.052 .

Lee, S. (2015). Analyzing negative SNS behaviors of elementary and middle school students in Korea. Computers in Human Behavior, 43, 15–27. https://doi.org/10.1016/j.chb.2014.10.014 .

Lim, J., & Richardson, J. C. (2016). Exploring the effects of students’ social networking experience on social presence and perceptions of using SNSs for educational purposes. The Internet and Higher Education, 29, 31–39. https://doi.org/10.1016/j.iheduc.2015.12.001 .

Lin, W.-Y., Zhang, X., Song, H., & Omori, K. (2016). Health information seeking in the Web 2.0 age: Trust in social media, uncertainty reduction, and self-disclosure. Computers in Human Behavior, 56, 289–294. https://doi.org/10.1016/j.chb.2015.11.055 .

Liu, C. C., Chen, Y. C., & Diana Tai, S. J. (2017). A social network analysis on elementary student engagement in the networked creation community. Computers and Education, 115 (300), 114–125. https://doi.org/10.1016/j.compedu.2017.08.002 .

Liu, D., & Brown, B. B. (2014). Self-disclosure on social networking sites, positive feedback, and social capital among Chinese college students. Computers in Human Behavior, 38, 213–219. https://doi.org/10.1016/j.chb.2014.06.003 .

Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017). Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Computers in Human Behavior, 70, 544–555. https://doi.org/10.1016/j.chb.2017.01.020 .

Mano, R. S. (2014). Social media and online health services: A health empowerment perspective to online health information. Computers in Human Behavior, 39, 404–412. https://doi.org/10.1016/j.chb.2014.07.032 .

Mazman, S. G., & Usluel, Y. K. (2010). Modeling educational usage of Facebook. Computers and Education, 55 (2), 444–453. https://doi.org/10.1016/j.compedu.2010.02.008 .

Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology . Cambridge: The MIT Press.

Meier, A., Reinecke, L., & Meltzer, C. E. (2016). Facebocrastination? Predictors of using Facebook for procrastination and its effects on students’ well-being. Computers in Human Behavior, 64, 65–76. https://doi.org/10.1016/j.chb.2016.06.011 .

Mirabolghasemi, M., Iahad, N. A., & Rahim, N. Z. A. (2016). Students’ perception towards the potential and barriers of social network sites in higher education. In Social networking and education (pp. 41–49). Switzerland: Springer. https://doi.org/10.1007/978-3-319-17716-8_3 .

Moghavvemi, S., Paramanathan, T., Rahin, N. M., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour and Information Technology, 36 (10), 1081–1100. https://doi.org/10.1080/0144929X.2017.1347201 .

Moghavvemi, S., Sharabati, M., Paramanathan, T., & Rahin, N. M. (2017). The impact of perceived enjoyment, perceived reciprocal benefits and knowledge power on students’ knowledge sharing through Facebook. International Journal of Management Education, 15 (1), 1–12. https://doi.org/10.1016/j.ijme.2016.11.002 .

Mostafa, R. B. (2015). Engaging students via social media: Is it worth the effort? Journal of Marketing Education, 37 (3), 144–159. https://doi.org/10.1177/0273475315585825 .

Nawi, N. B. C., Al Mamun, A., Nasir, N. A. B. M., Shokery, N. B., Raston, N. B. A., & Fazal, S. A. (2017). Acceptance and usage of social media as a platform among student entrepreneurs. Journal of Small Business and Enterprise Development, 24 (2), 375–393. https://doi.org/10.1108/JSBED-09-2016-0136 .

Ndasauka, Y., Hou, J., Wang, Y., Yang, L., Yang, Z., Ye, Z., et al. (2016). Excessive use of Twitter among college students in the UK: Validation of the Microblog Excessive Use Scale and relationship to social interaction and loneliness. Computers in Human Behavior, 55, 963–971. https://doi.org/10.1016/j.chb.2015.10.020 .

Nwagwu, W. E. (2017). Social networking, identity and sexual behaviour of undergraduate students in Nigerian universities. The Electronic Library, 35 (3), 534–558. https://doi.org/10.1108/EL-01-2015-0014 .

Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17 (10), 652–657. https://doi.org/10.1089/cyber.2014.0070 .

Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The influence of website characteristics on a consumer’s urge to buy impulsively. Information Systems Research, 20 (1), 60–78. https://doi.org/10.1287/isre.1070.0157 .

Park, N., Song, H., & Lee, K. M. (2014). Social networking sites and other media use, acculturation stress, and psychological well-being among East Asian college students in the United States. Computers in Human Behavior, 36, 138–146. https://doi.org/10.1016/j.chb.2014.03.037 .

Park, S. Y., Cha, S.-B., Lim, K., & Jung, S.-H. (2014). The relationship between university student learning outcomes and participation in social network services, social acceptance and attitude towards school life. British Journal of Educational Technology, 45 (1), 97–111. https://doi.org/10.1111/bjet.12013 .

Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Computers in Human Behavior, 28 (6), 2117–2127. https://doi.org/10.1016/j.chb.2012.06.016 .

Peters, A. N., Winschiers-Theophilus, H., & Mennecke, B. E. (2015). Cultural influences on Facebook practices: A comparative study of college students in Namibia and the United States. Computers in Human Behavior, 49, 259–271. https://doi.org/10.1016/j.chb.2015.02.065 .

Ponnusamy, S., Iranmanesh, M., Foroughi, B., & Hyun, S. S. (2020). Drivers and outcomes of Instagram addiction: Psychological well-being as moderator. Computers in Human Behavior, 107, 106294. https://doi.org/10.1016/j.chb.2020.106294 .

Rap, S., & Blonder, R. (2017). Thou shall not try to speak in the Facebook language: Students’ perspectives regarding using Facebook for chemistry learning. Computers and Education, 114, 69–78. https://doi.org/10.1016/j.compedu.2017.06.014 .

Raymond, J., & Wang, H. (2015). Social network sites and international students ’ cross-cultural adaptation. Computers in Human Behavior, 49, 400–411. https://doi.org/10.1016/j.chb.2015.03.041 .

Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. Internet and Higher Education, 13 (3), 134–140. https://doi.org/10.1016/j.iheduc.2010.03.002 .

Romero-Hall, E. (2017). Posting, sharing, networking, and connecting: Use of social media content by graduate students. TechTrends, 61 (6), 580–588. https://doi.org/10.1007/s11528-017-0173-5 .

Rui, J. R., & Wang, H. (2015). Social network sites and international students’ cross-cultural adaptation. Computers in Human Behavior, 49, 400–411. https://doi.org/10.1016/j.chb.2015.03.041 .

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation. Social Development, and Well-Being, 55 (1), 68–78.

Ryan, T., Chester, A., Reece, J., & Xenos, S. (2014). The uses and abuses of Facebook: A review of Facebook addiction. Journal of Behavioral Addictions, 3 (3), 133–148. https://doi.org/10.1556/JBA.3.2014.016 .

Serdyukov, P. (2017). Innovation in education: What works, what doesn’t, and what to do about it? Journal of Research in Innovative Teaching & Learning, 10 (1), 4–33. https://doi.org/10.1108/jrit-10-2016-0007 .

Sheeran, N., & Cummings, D. J. (2018). An examination of the relationship between Facebook groups attached to university courses and student engagement. Higher Education, 76, 937–955.

Shettar, M., Karkal, R., Kakunje, A., Mendonsa, R. D., & Chandran, V. V. M. (2017). Facebook addiction and loneliness in the post-graduate students of a university in southern India. International Journal of Social Psychiatry, 63 (4), 325–329. https://doi.org/10.1177/0020764017705895 .

Shim, M., Lee-Won, R. J., & Park, S. H. (2016). The self on the Net: The joint effect of self-construal and public self-consciousness on positive self-presentation in online social networking among South Korean college students. Computers in Human Behavior, 63, 530–539. https://doi.org/10.1016/j.chb.2016.05.054 .

Sin, S. C. J., & Kim, K. S. (2013). International students’ everyday life information seeking: The informational value of social networking sites. Library and Information Science Research, 35 (2), 107–116. https://doi.org/10.1016/j.lisr.2012.11.006 .

Singh, A. (2017). Mining of social media data of University students. Education and Information Technologies, 22 (4), 1515–1526. https://doi.org/10.1007/s10639-016-9501-1 .

Skues, J. L., Williams, B., & Wise, L. (2012). The effects of personality traits, self-esteem, loneliness, and narcissism on Facebook use among university students. Computers in Human Behavior, 28 (6), 2414–2419. https://doi.org/10.1016/j.chb.2012.07.012 .

Smith, R., Morgan, J., & Monks, C. (2017). Students’ perceptions of the effect of social media ostracism on wellbeing. Computers in Human Behavior, 68, 276–285. https://doi.org/10.1016/j.chb.2016.11.041 .

Special, W. P., & Li-Barber, K. T. (2012). Self-disclosure and student satisfaction with Facebook. Computers in Human Behavior, 28 (2), 624–630. https://doi.org/10.1016/j.chb.2011.11.008 .

Tally, S. (2010). Mixable blends Facebook with academics to improve student success . Purdue: Purdue University News.

Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 .

Tang, C. S. K., & Koh, Y. Y. W. (2017). Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder. Asian Journal of Psychiatry, 25, 175–178. https://doi.org/10.1016/j.ajp.2016.10.027 .

Tang, J. H., Chen, M. C., Yang, C. Y., Chung, T. Y., & Lee, Y. A. (2016). Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics, 33 (1), 102–108. https://doi.org/10.1016/j.tele.2015.06.003 .

Tashakkori, A., & Teddlie, C. (2003). Issues and dilemmas in teaching research methods courses in social and behavioural sciences: US perspective. International Journal of Social Research Methodology: Theory and Practice, 6 (1), 61–77. https://doi.org/10.1080/13645570305055 .

Teo, T., Doleck, T., & Bazelais, P. (2017). The role of attachment in Facebook usage: a study of Canadian college students. Interactive Learning Environments, 4820 (April), 1–17. https://doi.org/10.1080/10494820.2017.1315602 .

Tokunaga, R. S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior . https://doi.org/10.1016/j.chb.2009.11.014 .

Tower, M., Latimer, S., & Hewitt, J. (2014). Social networking as a learning tool: Nursing students’ perception of efficacy. Nurse Education Today, 34 (6), 1012–1017. https://doi.org/10.1016/j.nedt.2013.11.006 .

Van Hoof, J. J., Bekkers, J., & Van Vuuren, M. (2014). Son, you’re smoking on Facebook! College students’ disclosures on social networking sites as indicators of real-life risk behaviors. Computers in Human Behavior, 34, 249–257. https://doi.org/10.1016/j.chb.2014.02.008 .

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478. https://doi.org/10.2307/30036540 .

Walker, C. M., Sockman, B. R., & Koehn, S. (2011). An exploratory study of cyberbullying with undergraduate students. Tech Trends, 55 (2), 31–38. https://doi.org/10.1007/s11528-011-0481-0 .

Wang, J. C., & Chang, C. H. (2013). How online social ties and product-related risks influence purchase intentions: A Facebook experiment. Electronic Commerce Research and Applications, 12 (5), 337–346.

Wang, J. L., Jackson, L. A., Gaskin, J., & Wang, H. Z. (2014). The effects of Social Networking Site (SNS) use on college students’ friendship and well-being. Computers in Human Behavior, 37, 229–236. https://doi.org/10.1016/j.chb.2014.04.051 .

Wang, J. L., Jackson, L. A., Zhang, D. J., & Su, Z. Q. (2012a). The relationships among the Big Five Personality factors, self-esteem, narcissism, and sensation-seeking to Chinese University students’ uses of social networking sites (SNSs). Computers in Human Behavior, 28 (6), 2313–2319. https://doi.org/10.1016/j.chb.2012.07.001 .

Wang, X., Li, Y., & Wang, X. (2016). Users’ satisfaction with social network sites: A self-determination perspective. Journal of Computer Information Systems , 4417 (February). https://doi.org/10.1080/08874417.2015.11645800

Wang, X., Lin, X., & Spencer, M. K. (2019). Exploring the effects of extrinsic motivation on consumer behaviors in social commerce: Revealing consumers’ perceptions of social commerce benefits. International Journal of Information Management, 45 (March 2018), 163–175. https://doi.org/10.1016/j.ijinfomgt.2018.11.010 .

Wang, Z., Tchernev, J. M., & Solloway, T. (2012b). A dynamic longitudinal examination of social media use, needs, and gratifications among college students. Computers in Human Behavior, 28 (5), 1829–1839. https://doi.org/10.1016/j.chb.2012.05.001 .

Webster, J., & Watson, R. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26 (2), 13–23.

Wickramanayake, L., & Muhammad Jika, S. (2018). Social media use by undergraduate students of education in Nigeria: A survey. The Electronic Library, 36 (1), 21–37. https://doi.org/10.1108/EL-01-2017-0023 .

Wodzicki, K., Schwämmlein, E., & Moskaliuk, J. (2012). “Actually, I Wanted to Learn”: Study-related knowledge exchange on social networking sites. Internet and Higher Education, 15 (1), 9–14. https://doi.org/10.1016/j.iheduc.2011.05.008 .

Wohn, D. Y., & Larose, R. (2014). Effects of loneliness and differential usage of Facebook on college adjustment of first-year students. Computers and Education, 76, 158–167. https://doi.org/10.1016/j.compedu.2014.03.018 .

Yazdanparast, A., Joseph, M., & Qureshi, A. (2015). An investigation of Facebook boredom phenomenon among college students. Young Consumers, 16 (4), 468–480. https://doi.org/10.1108/YC-02-2015-00506 .

Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information Management, 51, 1017–1030.

Zhang, K. Z., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108.

Zhang, R. (2017). The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, social support, and mental health among college students. Computers in Human Behavior, 75, 527–537. https://doi.org/10.1016/j.chb.2017.05.043 .

Download references

Acknowledgements

This paper is supported by Fundamental Research Grant Scheme (FRGS) (Vote No. R.K130000.7840.4F245), and UTM Razak School of Engineering and Advanced Technology research grant or DPUTMRAZAK (Vote No. R.K13000.7740.4J313).

Author information

Authors and affiliations.

Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia

Maslin Binti Masrom & Nik Hasnaa Nik Mahmood

Irish Institute of Digital Business, DCU Business School, Dublin City University, Dublin 9, Ireland

Abdelsalam H. Busalim

Faculty of Social Sciences & Humanities, School of Education, Universiti Teknologi Malaysia, UTM, 81310, Skudai, Johor, Malaysia

Hassan Abuhassna

You can also search for this author in PubMed   Google Scholar

Contributions

The corresponding author worked in writing the paper, all authors worked collaboratively to write the literature review and discussion. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Abdelsalam H. Busalim .

Ethics declarations

Competing interests.

This paper is an original work, this paper conducted a systematic literature review of students’ behavior and OSNs studies to explicate to what extent students behave on these platforms.

Additional information

Publisher's note.

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

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

Masrom, M.B., Busalim, A.H., Abuhassna, H. et al. Understanding students’ behavior in online social networks: a systematic literature review. Int J Educ Technol High Educ 18 , 6 (2021). https://doi.org/10.1186/s41239-021-00240-7

Download citation

Received : 16 July 2020

Accepted : 13 January 2021

Published : 30 January 2021

DOI : https://doi.org/10.1186/s41239-021-00240-7

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Students’ behavior
• Social media
• Systematic review
• Stimulus-organism-response model

• Open access
• Published: 31 October 2023

Social media usage and students’ social anxiety, loneliness and well-being: does digital mindfulness-based intervention effectively work?

BMC Psychology volume  11 , Article number:  362 ( 2023 ) Cite this article

16k Accesses

2 Citations

35 Altmetric

Metrics details

The increasing integration of digital technologies into daily life has spurred a growing body of research in the field of digital psychology. This research has shed light on the potential benefits and drawbacks of digital technologies for mental health and well-being. However, the intricate relationship between technology and psychology remains largely unexplored.

This study aimed to investigate the impact of mindfulness-based mobile apps on university students' anxiety, loneliness, and well-being. Additionally, it sought to explore participants' perceptions of the addictiveness of these apps.

The research utilized a multi-phase approach, encompassing a correlational research method, a pretest–posttest randomized controlled trial, and a qualitative case study. Participants were segmented into three subsets: correlations ( n  = 300), treatment ( n  = 60), and qualitative ( n  = 20). Data were gathered from various sources, including the social anxiety scale, well-being scale, social media use integration scale, and an interview checklist. Quantitative data was analyzed using Pearson correlation, multiple regression, and t-tests, while qualitative data underwent thematic analysis.

The study uncovered a significant correlation between social media use and the variables under investigation. Moreover, the treatment involving mindfulness-based mobile apps led to a reduction in students' anxiety and an enhancement of their well-being. Notably, participants held various positive perceptions regarding the use of these apps.

Implications

The findings of this research hold both theoretical and practical significance for the field of digital psychology. They provide insight into the potential of mindfulness-based mobile apps to positively impact university students' mental health and well-being. Additionally, the study underscores the need for further exploration of the intricate dynamics between technology and psychology in an increasingly digital world.

Peer Review reports

Introduction

The field of digital psychology is undergoing rapid evolution, navigating the intricate intersection of psychology and technology to elucidate the profound impact of digital technologies on human behavior, cognition, and emotions [ 1 , 2 ]. With digital technologies becoming increasingly ingrained in our daily lives, researchers are embarking on a journey to explore the multifaceted implications they bear for mental health and overall well-being. Within the realm of digital psychology, a diverse array of topics has captured the attention of investigators, encompassing the innovative use of technology for psychological interventions like cognitive-behavioral therapy (CBT) and mindfulness-based stress reduction (MBSR) [ 1 , 2 ]. Furthermore, scrutiny has extended to the influence of social media on mental health, unveiling the potential for excessive social media use to contribute to feelings of anxiety and loneliness [ 3 , 4 ].

The exploration of digital psychology has also delved into the impact of video games on cognitive and emotional faculties, with some studies suggesting that specific genres of video games have the potential to enhance attention and problem-solving skills [ 5 , 6 ]. However, concerns surrounding video game addiction and the potential influence of violent video games on aggressive behavior have been the subject of extensive investigation [ 7 , 8 , 9 , 10 ]. The ubiquity of digital technologies in our daily existence has ignited a burgeoning interest in the domain of digital psychology. While research in this domain has yielded valuable insights into the prospective benefits and hazards of digital technologies for mental health and well-being, there remains a vast expanse of knowledge yet to be uncovered regarding the intricate interplay between technology and psychology. Specifically, there is a compelling need for an extensive body of research aimed at comprehending the enduring impacts of digital technologies on cognitive, emotional, and social functionality. Furthermore, it is crucial to decipher how these effects may vary among diverse demographic groups.

One particularly promising avenue of research within digital psychology is the integration of mindfulness-based mobile applications, which has shown considerable potential in alleviating symptoms of anxiety and loneliness. These applications typically offer guided meditation, breathing exercises, and various mindfulness practices that are readily accessible via mobile devices [ 2 ]. Their accessibility and user-friendly nature render them an appealing resource for individuals seeking to enhance their mental well-being without the need for traditional face-to-face therapy [ 3 , 6 ].

In the contemporary landscape of higher education, university students are exposed to the pervasive influence of social media, which has the potential to induce negative psychological consequences such as heightened social anxiety and increased feelings of loneliness. The omnipresence of social media platforms can foster a sense of comparison, social pressure, and disconnection among undergraduate students, amplifying the challenges they already face. Given these circumstances, there is a compelling need to explore interventions that can counteract these adverse impacts, and mindfulness-based interventions emerge as a promising avenue for consideration.

By examining the intersection of these interventions with the digital sphere, this study seeks to illuminate how Digital Mindfulness-based treatments might serve as a potent tool to mitigate the detrimental effects of social media exposure, thereby fostering a healthier psychological landscape among university students [ 11 , 12 , 13 , 14 , 15 ].

Furthermore, many of these applications provide personalized features such as progress tracking and goal setting, which enhance user engagement and motivation [ 9 ]. As the popularity of these applications continues to soar, it becomes imperative to further investigate their effectiveness across various demographic cohorts and contextual settings, as well as to identify the most potent features and interventions for fostering improvements in mental health [ 10 ].

The rationale for this study is firmly grounded in the contemporary higher education landscape, where undergraduate students navigate a myriad of challenges that may impact their mental well-being. With the pervasive integration of digital technologies into students' lives, the investigation of Digital Mindfulness-based interventions becomes not only relevant but crucial. The novelty of this study lies in its exploration of the intricate relationship between social media usage and the well-being of university students, specifically targeting social anxiety and loneliness. Moreover, it introduces an innovative approach by examining the effectiveness of digital mindfulness-based interventions in ameliorating these psychological challenges. By addressing this uncharted territory, the study not only contributes to the growing field of digital psychology but also offers valuable insights into the potential of technology-driven mindfulness interventions as a means to enhance the mental well-being of the digital-native student population. This unique blend of investigating the impact of technology on psychological well-being while simultaneously assessing the effectiveness of digital interventions positions the study at the forefront of contemporary research in the field. Given the potential benefits of digital mindfulness apps in reducing anxiety and loneliness, coupled with the distinct challenges that emerge during the undergraduate phase, this research seeks to provide invaluable insights into the perceptions and experiences of students. By delving into the perceptions of adults regarding these treatments, this study aspires to shed light on the feasibility, effectiveness, and potential limitations of digital mindfulness-based interventions for enhancing the mental health of undergraduate students in the modern digital age. Therefore, this study endeavors to address the following critical questions:

What is the relationship between social media use and symptoms of social anxiety, loneliness, and well-being among university students?

Does the use of a mindfulness-based mobile app intervention result in significant improvements in social anxiety, loneliness, and well-being in college students?

What are university students’ perspectives on the use of technology for mental health support, including the benefits and challenges of using technology for this purpose?

Review of literature

Theoretical background.

The study investigating the effects of mindfulness-based mobile apps on university students' anxiety, loneliness, and well-being in the context of social media usage draws upon a multifaceted theoretical framework. At its core, it is rooted in mindfulness theory, which emphasizes present-moment awareness and non-judgmental acceptance to alleviate stress and anxiety [ 5 , 6 , 7 , 8 ]. To understand the influence of social media on students, social cognitive theory is relevant, as it explores how individuals learn from observing others in their social networks. Additionally, social comparison theory informs the study by shedding light on how students may constantly compare themselves to others on social media, potentially leading to feelings of loneliness and social anxiety [ 11 , 12 , 13 , 14 , 15 ]. The study also taps into addiction and compulsive behavior theories to comprehend the perceived addictiveness of mindfulness-based mobile apps. Technology acceptance models (TAM) help in understanding user acceptance and perceptions of these apps. Moreover, the study aligns with principles of positive psychology by aiming to enhance well-being and reduce anxiety and loneliness, which are central concerns in this field. Finally, media effects theories, like cultivation theory and uses and gratifications theory, inform the exploration of how social media use affects students' mental health and well-being [ 13 ]. This multifaceted theoretical approach provides a comprehensive foundation for unraveling the intricate relationship between technology, psychology, and well-being in the digital age, offering a well-rounded perspective on the research questions at hand [ 12 , 13 ].

Social media and symptoms of mental health

The use of social media has become increasingly prevalent among university students, and with it comes growing concern about its potential impact on mental health and well-being. Specifically, research has focused on the relationship between social media use and symptoms of social anxiety, loneliness, and well-being among university students. The majority of studies focused on the relationship between social media use and symptoms of social anxiety and/or loneliness. These studies generally found that higher levels of social media use were associated with greater symptoms of social anxiety and loneliness among university students [ 11 , 12 , 13 , 14 , 15 , 16 ]. For example, Schønning et al. [ 16 ] found that social media use was positively associated with symptoms of social anxiety among Chinese university students. Similarly, a study by Wang et al. [ 13 ] found that social media use was positively associated with symptoms of loneliness among Chinese university students.

Two studies focused on the relationship between social media use and well-being. One study found that higher levels of social media use were associated with lower levels of well-being among university students [ 17 ] Another study found that social media use had a curvilinear relationship with well-being, such that moderate levels of social media use were associated with higher levels of well-being, while both low and high levels of social media use were associated with lower levels of well-being [ 13 ].

The findings of this literature review suggest that social media use may be associated with greater symptoms of social anxiety and loneliness among university students. However, the relationship between social media use and well-being is less clear, with some studies suggesting a negative relationship and others suggesting a curvilinear relationship. Several additional studies have also examined this relationship. For example, a study by Kose and Dogan [ 18 ] found that social media use was negatively associated with psychological well-being among Turkish university students. Another study by Błachnio, et al., [ 19 ] found that Facebook addiction was negatively associated with self-esteem and life satisfaction among Polish university students. Similarly, Chen et al. [ 20 ] conducted a systematic review of 23 studies examining the relationship between social media use and mental health outcomes among college students. The authors concluded that social media use was generally associated with negative mental health outcomes, including loneliness, anxiety, and stress. However, they noted that the strength of this relationship varied across studies and suggested that more research was needed to better understand the mechanisms underlying this relationship. In another study, Seabrook et al. [ 21 ] conducted a systematic review of 20 studies examining the relationship between social networking sites and loneliness and anxiety. They found that social networking sites were associated with both loneliness and anxiety, but that the strength of this relationship varied across studies and depended on factors such as frequency and intensity of social networking site use and individual differences in vulnerability to mental health problems. Similarly, Tandoc Jr. et al. [ 14 ] conducted a study examining the relationship between Facebook use, envy, and depression among college students in the United States. They found that Facebook use was positively associated with envy, which in turn was positively associated with depression. They suggested that envy may be a mechanism underlying the relationship between social media use and negative mental health outcomes.

Mindfulness-based apps effect mental health

Mindfulness-based mobile apps are becoming increasingly popular as a tool for promoting mental health and wellbeing. These apps include a variety of different mindfulness-based practices, such as guided meditations, breathing exercises, and other techniques aimed at reducing stress and anxiety. While there is growing evidence that mindfulness-based interventions can be effective in promoting mental health, less is known about the effectiveness of these interventions when delivered via mobile apps. This literature review aims to synthesize the existing research on mindfulness-based mobile apps and mental health outcomes.

The majority of studies focused on the effectiveness of mindfulness-based mobile apps in reducing symptoms of anxiety and depression. These studies generally found that mindfulness-based mobile apps were effective in reducing symptoms of anxiety and depression in a variety of populations, including college students, adults, and individuals with chronic medical conditions [ 2 , 10 , 22 , 23 , 24 ]. For example, a study by Strauss et al. [ 23 ] found that a mindfulness-based mobile app was effective in reducing stress and improving coping skills in a sample of healthcare workers. Similarly, a study by Lomas et al. [ 24 ] found that a mindfulness-based mobile app was effective in reducing stress and improving resilience in a sample of university students. In addition to examining the effectiveness of mindfulness-based mobile apps, several studies explored the factors that influence user engagement and adherence to these interventions. For example, a study by Valinskas et al. [ 25 ] that users who were using the app for more than 24 days and had at least 12 active days during that time had 3.463 (95% CI 1.142–11.93) and 2.644 (95% CI 1.024–7.127) times higher chances to reduce their DASS-21 subdomain scores of depression and anxiety, respectively. Another study by Linardon, et al. [ 22 ] found that interventions that were more interactive and personalized were more effective in promoting user engagement and adherence.

Some studies also explored the effectiveness of mindfulness-based mobile apps in addressing other mental health conditions beyond anxiety and depression. For example, a study by Wahbeh et al. [ 10 ] found that a mindfulness-based mobile app intervention was effective in reducing symptoms of posttraumatic stress disorder (PTSD) in a sample of veterans. Similarly, a study by Biegel et al. [ 26 ] found that a mindfulness-based mobile app intervention was effective in reducing symptoms of ADHD in a sample of adolescents.

The use of technology for mental health support

The utilization of technology for the provision of mental health support has gained increasing prominence within the context of university students, prompting a burgeoning interest in comprehending their encounters and viewpoints. Related inquiries have been undertaken in diverse geographical regions, including the United States, Canada, Australia, and the United Kingdom. Predominantly, these investigations have centered on the advantages and obstacles inherent in employing technology for mental health support. Generally, these inquiries have ascertained that technology is perceived as a convenient and readily accessible modality for accessing mental health support services among university students [ 27 , 28 , 29 , 30 ]. For instance, Birnbaum et al. [ 27 ] conducted a study revealing that college students in the United States exhibited a willingness to engage with mental health applications to manage their stress and anxiety. Nevertheless, certain studies have also discerned impediments associated with the adoption of technology for mental health support, encompassing apprehensions regarding privacy and confidentiality [ 27 , 28 , 29 , 30 ], concerns about the quality and dependability of information [ 29 ], and challenges related to navigating and effectively utilizing mental health applications [ 30 ].

Additionally, two investigations have focused their attention on delineating the determinants influencing the utilization of technology for mental health support among university students. These studies have identified an array of factors exerting an influence over students' engagement with technology for mental health support, encompassing individual attributes (e.g., mental health literacy, technological attitudes) [ 31 ], societal influences (e.g., stigma, peer support) [ 31 ], and environmental considerations (e.g., technology availability, access to mental health services). The cumulative insights garnered from this comprehensive literature review underscore the potential of technology as a convenient and accessible avenue for accessing mental health support among university students. However, it is essential to acknowledge that complexities and multifaceted dynamics underlie the factors influencing its utilization, and an array of challenges remain associated with its application in this context.

Likewise, a study conducted by Kern et al. [ 32 ] documented that 23.8% of users reported experiencing a positive impact on their mental health through the use of mental health applications. Notably, individuals who had received mental health services within the past 12 months exhibited a significantly higher propensity to embrace mental health apps in comparison to those who had not accessed such services. The allure of convenience, immediate availability, and confidentiality emerged as prevalent factors driving interest in Mental Health Apps (MHAs).

Furthermore, a study conducted by Free et al. [ 33 ] unveiled the unsurprising proliferation of numerous mobile applications designed to aid in the diagnosis, monitoring, and management of health conditions, albeit with varying levels of efficacy. Similarly, research by Brindal et al. [ 34 ] found that participants who had intermittent access to a smartphone app over a 4-week trial period demonstrated notable enhancements in indicators of emotional well-being. This broader observation suggests that uncomplicated and easily accessible solutions can yield substantial improvements in overall well-being. In addition, a study by Karyotaki et al. [ 35 ] reported the effectiveness of web-based interventions in mitigating the symptoms of depression and anxiety among college students.

Methodology

This was a multi-phase research design. In the first phase, a correlational research method was used for exploring the correlation among the research variables. In the second phase, we used a pretest–posttest randomized controlled trial to assess the effectiveness of a mindfulness-based mobile app intervention on symptoms of anxiety, loneliness, and well-being. Moreover, in the third phase, a qualitative research method was used for exploring the participants’ perceptions of mindfulness-based intervention.

Participants

Participants for this study were selected from graduate students at Zhoukou Vocational and Technical College in China. Three separate groups were recruited for the study. The first group consisted of 300 participants who were recruited for a correlational study related to question 1. The eligibility criteria for this group were as follows: participants must be graduate students at Fudan University and willing to participate in the study. The sample size was determined based on power analysis and the expected effect size. The second group consisted of 100 participants who were recruited for question 2. The eligibility criteria for this group were the same as for the first group. Participants were randomly assigned to either an intervention group or a control group. The third group consisted of 20 participants who were recruited for question 3. The eligibility criteria for this group were the same as for the first two groups. Participants were selected using purposive sampling based on their responses to the questionnaire in question 2. All participants provided informed consent prior to participating in the study. The study was approved by the Institutional Review Board at Zhoukou Vocational and Technical College. Participants were assured of confidentiality and the right to withdraw from the study at any time without penalty.

The following instruments were used to collect data for this study:

Social Anxiety Scale for Adolescents (SAS-A)

It is a 22-item self-report questionnaire that measures social anxiety in adolescents [ 36 ]. SAS-A assesses various aspects of social anxiety, including fear of negative evaluation, social avoidance and distress, and physiological symptoms such as sweating and blushing. Each item is measured on a 5-point Likert scale, ranging from 1 (not at all) to 5 (extremely). The total score on the SAS-A ranges from 22 to 110, with higher scores indicating higher levels of social anxiety.

Warwick-Edinburgh Mental Well-being Scale (WEMWBS)

It is a 14-item self-report questionnaire that measures mental well-being in adults and adolescents [ 37 ]. The items on the WEMWBS assess various aspects of mental well-being, including optimism, positive relationships, and a sense of purpose. Participants rate each item on a 5-point Likert scale, ranging from 1 (none of the time) to 5 (all of the time). The total score on the WEMWBS ranges from 14 to 70, with higher scores indicating higher levels of mental well-being. The fourth instrument was social.

Social Media Use Integration Scale (SMUIS)

The SMUIS is a 10-item self-report questionnaire that assesses the frequency, duration and emotional connection to social media use [ 38 ]. The SMUIS includes questions related to the frequency and duration of social media use, as well as questions related to the emotional connection to social media use, such as "How often do you feel happy when using social media?" and "How often do you feel anxious when you are not able to use social media?" Participants are asked to rate each item on a 5-point Likert scale, ranging from 1 (never) to 5 (always). The reliability of the instruments was estimated using Cronbach’s alpha. Results revealed that the obtained Cronbach’s alpha for the instrument was above, 0.78 indicating that all used instruments enjoyed an acceptable level of reliability.

Interview checklist

The interview checklist consisted of 8 open-ended questions followed by the interviewer’s prompts. The questions elicited the interviewees’ perceptions of the benefits and challenges of using mobile apps for improving mental health and well-being and reducing social anxiety symptoms and loneliness (See Additional file 1 ). The interview checklist was approved by 4 colleagues and there was a high agreement among the panel of experts regarding the relevance of the interview questions.

Mindfulness-based mobile apps

Mindfulness-based mobile apps are mobile applications designed to help individuals develop mindfulness skills and reduce symptoms of stress, anxiety, and depression. These apps typically include guided mindfulness exercises, educational resources, and other features to help individuals practice mindfulness on a regular basis. The specific features of mindfulness-based mobile apps may vary but typically include guided meditations, breathing exercises, and other mindfulness practices. Some apps may also include educational resources, such as articles or videos that provide information about mindfulness and its benefits. Many apps also include features for tracking progress, setting goals, and sharing progress with others. In this study, the participants who participated in the treatment phase were asked to download popular mindfulness-based mobile apps including Headspace, Calm, and Insight Timer. These apps are available for download on mobile devices and offer a range of mindfulness exercises and resources for users to explore.

The study was conducted in multiple steps. Initially, a sample of 300 graduate students from Fudan University was selected to participate in the research. These participants were asked to complete the Social Media Use Integration Scale (SMUIS) and the Depression Anxiety Stress Scales (DASS-21) to evaluate their social media use and mental health status. Next, a sample of 60 students from the same university was selected for the intervention study. These participants were randomly assigned to either an intervention group or a control group. The intervention group was given access to a mindfulness-based mobile app for eight weeks, while the control group received no intervention. Both groups completed the SMUIS and the DASS-21 at baseline, post-intervention, and three-month follow-up to evaluate the effectiveness of the intervention. Lastly, a qualitative study was conducted to gather in-depth information about the participants' experience with the mindfulness-based mobile app intervention. A purposive sample of 20 participants from the intervention group was selected for this study. They underwent semi-structured interviews to provide qualitative data about their perceptions and opinions regarding the intervention.

Data analysis

For the quantitative data, the statistical software was employed. Firstly, descriptive statistics were calculated to determine the mean, and standard deviation of the Social Media Use Integration Scale (SMUIS) and Depression Anxiety Stress Scales (DASS-21) scores, as well as the mean, and standard deviation of the SMUIS and DASS-21 scores at baseline, post-intervention, and three-month follow-up for both the intervention and control groups. Secondly, bivariate correlations were conducted to examine the relationship between social media use and symptoms of anxiety and depression. Thirdly, multiple regression analysis was performed to determine the unique contribution of social media use to symptoms of anxiety and depression while controlling for other relevant variables. Fourthly, repeated measures ANOVA was conducted to examine changes in SMUIS and DASS-21 scores over time and to determine if there were differences between the intervention and control groups. Finally, post hoc tests were conducted to examine differences between groups at each time point. Effect sizes were calculated to determine the magnitude of the intervention's effects. However, for the qualitative data, the qualitative analysis software was employed. Firstly, the transcripts of the semi-structured interviews were analyzed using thematic analysis to identify themes and subthemes related to participants' experiences with the mindfulness-based mobile app intervention. Secondly, quotes were selected to support and illustrate the identified themes and subthemes. Lastly, the themes and subthemes were interpreted and discussed to provide insight into participants' perceptions and opinions regarding the intervention.

Research question1

Pearson correlations between the variables were estimated and results are presented in Table 1 .

This table shows that social media use is negatively correlated with well-being ( r  = -0.21, p  < 0.01) and positively correlated with symptoms of social anxiety ( r  = -0.35, p  < 0.01) and loneliness ( r  = 0.24, p  < 0.01). Additionally, symptoms of social anxiety are positively correlated with loneliness ( r  = 0.47, p  < 0.01) and negatively correlated with well-being ( r  = -0.61, p  < 0.01), while loneliness is negatively correlated with well-being ( r  = -0.50, p  < 0.01). These results suggest that social media use is associated with poorer mental health outcomes, including higher levels of social anxiety and loneliness and lower levels of well-being, among university students.

Table 2 shows the results of a multiple regression analysis that examined the relationship between social media use, social anxiety, and loneliness as predictor variables and well-being as the outcome variable. The regression equation is:

The results indicate that all three predictor variables significantly contributed to the prediction of well-being, with social media use (β = -0.29, p  = 0.001), social anxiety (β = 0.31, p  = 0.001), and loneliness (β = 0.28, p  = 0.001) each having a significant unique effect on well-being, after controlling for the other variables. The constant term (B = 3.10, p  = 0.001) represents the predicted well-being score when all predictor variables are held at zero.

Research question 2

The second research aimed at investigating the effects of the intervention on the students’ social anxiety, loneliness, and well-being. Results are presented in Table 3 .

This table presents the results of a pretest–posttest randomized control-experimental research design investigating the effects of a mindfulness-based mobile app intervention on social anxiety, loneliness, and well-being in college students. The results indicate that the intervention group showed a significant improvement in social anxiety (F (1, 98) = 17.23, p  < 0.001, partial eta squared = 0.15), loneliness (F (1, 98) = 13.70, p  < 0.001, partial eta squared = 0.12), and well-being (F(1, 98) = 21.41, p  < 0.001, partial eta squared = 0.18) from pretest to posttest. The control group did not show significant changes in any of the measures. The effect sizes (partial eta squared) ranged from moderate to large, indicating that the intervention had a meaningful impact. These findings suggest that the use of a mindfulness-based mobile app intervention can be an effective approach for improving mental health outcomes in college students.

Research question 3

The third research question explored the students’ perceptions of the effects of mindfulness-based mobile apps on the students’ social anxiety, loneliness, and well-being. The detailed analysis of the interviews revealed 6 benefits and 4 challenges of using technology for mental health support. The first extracted benefit as mentioned by 10 students was thematically coded "Convenience and Accessibility". Participants reported that technology-based mental health support services are convenient and accessible, allowing them to access support anytime and anywhere. The following quotations exemplify the theme:

"I like using mental health apps because I can access them whenever I need to. I don't have to wait for an appointment or anything like that." (Student 3). Another student stated, "Online support groups are great because I can connect with people who have similar experiences no matter where I am."(student 11).

The second extracted benefit was thematically coded "Anonymity and Privacy". Participants appreciated the ability to access mental health support services online while maintaining anonymity and privacy. For instance, student 5 stated, "I like that I can access support without having to go to an office or talk to someone face-to-face. It feels less intimidating." This finding was also confirmed by student 6, who stated, "I feel more comfortable talking about my mental health online because I know that no one else needs to know about it."

The third extracted benefit was thematically coded "Customizable and Tailored Support". Participants appreciated the range of options available for mental health support online, including customizable and tailored support that they could access at their own pace. For instance, student 11 stated, "I like that I can choose the type of support that works for me. Some days I just need to read something and other days I need to talk to someone”. Similarly, student 6 stated, "The mental health app I use sends me reminders to check in with myself and practice self-care. It's nice to have that kind of tailored support."

The fourth extracted benefit was thematically coded as "Cost-effective". Participants reported that technology-based mental health support services are often more affordable than traditional face-to-face therapy, making them a more accessible option for those with limited financial resources. This finding was supported by student 17 who stated, "I can't afford traditional therapy, so using mental health apps is a great option for me since it's usually free or very affordable." Similarly, one of the students stated, “Online therapy is much cheaper than traditional therapy, so it's more accessible for people who can't afford to pay a lot."

The fifth extracted benefit was thematically coded as "Increased Awareness and Education". Participants reported that technology-based mental health support services helped them to become more aware of their mental health and provided education about mental health issues and coping strategies. For example, student 12 stated, "The mental health app I use has taught me a lot about mindfulness and how to manage my anxiety." Student 14 also stated, "I learned a lot about depression and how to cope with it from an online support group I joined."

The sixth extracted benefit was thematically coded as "Reduced Stigma". Participants reported that accessing mental health support services online helped to reduce the stigma associated with seeking mental health The following quotations exemplify the theme of support. For instance, one of the students stated, “I used to feel ashamed about seeking mental health support, but using mental health apps has helped me realize that it's okay to take care of my mental health." (Student 9). Similarly, another student argued, “Online support groups have helped me realize that I'm not alone in my struggles with mental health. It's nice to know there are others out there who understand."

Despite the above-mentioned benefits, the participants mentioned some challenges. The first extracted challenge was thematically coded "Quality and Accuracy of Information". Participants expressed concerns about the quality and accuracy of mental health information available online, and the potential for misinformation to be spread. For instance, student 11 stated, "There's so much information online, it's hard to know what's trustworthy and what's not." Another student stated, "I worry that some of the mental health information I see online is not based on evidence and could actually be harmful."(student 6).

The second extracted challenge was thematically coded as "Lack of Human Connection". Participants reported missing the human connection they would get from traditional face-to-face therapy and felt that technology-based mental health support services lacked the same level of personal connection. The following quotations from student 12 exemplify the theme:

"Sometimes I just need someone to talk to face-to-face. It's not the same as talking to a computer screen…. I miss the empathetic listening I would get from a therapist in person. It's hard to replicate that online."

The third extracted challenge was thematically coded as "Technical Difficulties". Participants reported experiencing technical difficulties with technology-based mental health support services, which could be frustrating and hinder their ability to access support. For instance, student 8 stated, “Sometimes the mental health app I use glitches or crashes, which can be really frustrating when I'm trying to use it for support…. I don't have the best internet connection, so sometimes it's hard to access online support groups."

The fourth extracted challenge was thematically coded "Privacy and Security Concerns". Participants expressed concerns about the privacy and security of their personal information when using technology-based mental health support services, and whether their information was being shared without their consent. As an example, student 13 stated, "I worry that my personal information could be shared without my consent, which would be a huge breach of trust." Student 9 also stated, “It's hard to know if my information is really secure when I'm using online mental health support services."

The study investigating the effects of mindfulness-based mobile apps on university students' anxiety, loneliness, and well-being in the context of social media usage is anchored in a multifaceted theoretical framework. At its core, the research draws upon mindfulness theory, a foundational framework emphasizing present-moment awareness and non-judgmental acceptance to alleviate stress and anxiety [ 5 , 6 , 7 , 8 ]. This theory forms the bedrock of the study's understanding, as mindfulness-based mobile apps are designed to foster these very principles, encouraging users to engage with the present, accept their experiences without judgment, and, in doing so, mitigate stress and anxiety.

In parallel, to fathom the intricate influence of social media on university students, the study leverages social cognitive theory, a framework highly pertinent for analyzing how individuals acquire and adapt behaviors, attitudes, and emotional responses through observation and modeling within their social networks [ 11 , 12 , 13 , 14 , 15 ]. Given the pervasive role of social media, this theory is essential for comprehending how the behaviors, emotions, and attitudes of students may be shaped by the content and interactions they encounter in the digital realm.

Moreover, the research takes into consideration social comparison theory, which underscores how social media users frequently engage in relentless self-comparisons with others, potentially fostering feelings of loneliness and social anxiety [ 11 , 12 , 13 , 14 , 15 ]. This theory is critical for acknowledging the "highlight reel" effect, wherein users predominantly share their positive experiences and achievements, inadvertently prompting social comparison and potentially engendering negative emotional responses.

In the exploration of the perceived addictiveness of mindfulness-based mobile apps, the study employs addiction and compulsive behavior theories. These theories unearth the underlying factors contributing to the allure and habit-forming nature of certain digital interventions, thereby offering valuable insights into the psychology of user engagement and potential addiction [ 12 , 13 ]. When assessing user acceptance and perceptions of mindfulness-based mobile apps, the study draws from technology acceptance models (TAM). TAM provides a valuable framework for unraveling the intricacies of user adoption and attitudes toward technology-based interventions, elucidating critical factors like perceived usefulness and ease of use, which shed light on participants' acceptance of these apps [ 12 , 13 ].

Furthermore, the research aligns with the principles of positive psychology, a framework that centers on the enhancement of human well-being and strengths. The study's focus on bolstering well-being and mitigating anxiety and loneliness aligns closely with the core tenets of positive psychology, making it a pertinent theoretical perspective [ 12 , 13 ].

Lastly, media effects theories, such as cultivation theory and uses and gratifications theory, play a pivotal role in offering insights into how social media usage affects students' mental health and well-being [ 13 ]. Cultivation theory underscores the potential long-term impact of repeated exposure to media content, while uses and gratifications theory delves into how individuals actively use and engage with media to fulfill specific needs and gratifications.

By encompassing this multifaceted theoretical approach, the study constructs a comprehensive foundation for unraveling the intricate relationship between technology, psychology, and well-being in the digital age. This holistic perspective serves as a valuable compass in navigating the complexities of the research questions at hand, offering a deeper understanding of how these factors interconnect and influence one another [ 12 , 13 ]. Additionally, the study incorporates media effects theories to further enrich its theoretical foundation. Cultivation theory, as one of the key media effects theories, underlines the potential long-term consequences of repeated exposure to media content. Given the omnipresence of social media in the lives of university students, understanding how continuous media exposure might shape their perceptions and attitudes is crucial [ 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Moreover, uses and gratifications theory plays a pivotal role by exploring how individuals actively engage with media to fulfill specific needs and gratifications. In the context of the study, this theory sheds light on why students turn to social media, whether it's for social interaction, information seeking, or entertainment, and how these purposes might be linked to their mental well-being [ 13 ].

To round out the comprehensive theoretical framework, the study interweaves elements of positive psychology. This perspective emphasizes the enhancement of human well-being, positive emotions, and strengths. By striving to boost well-being and alleviate symptoms of anxiety and loneliness, the study directly aligns with the core principles of positive psychology. Positive psychology focuses on fostering qualities like resilience, optimism, and emotional intelligence, which are highly relevant to the study's objectives [ 46 , 47 , 48 , 49 , 50 ]. Thus, this framework adds a positive, growth-oriented dimension to the study's theoretical foundation, underscoring the importance of not only addressing negative mental health outcomes but also promoting positive psychological well-being [ 12 , 13 ].

In summary, the multifaceted theoretical framework encompassing mindfulness theory, social cognitive theory, social comparison theory, addiction and compulsive behavior theories, technology acceptance models (TAM), positive psychology, and media effects theories creates a robust and comprehensive foundation for unraveling the intricate relationship between technology, psychology, and well-being in the digital age. This holistic perspective enables the study to navigate the complexities of its research questions, offering a deeper understanding of how these factors interconnect and influence one another, and providing valuable insights into the impact of technology-driven interventions on the mental well-being of university students.

Conclusions

It can be concluded that the current findings add to the growing body of literature suggesting that social media use is linked to negative mental health outcomes. However, it is important to note that the causal direction of these relationships remains unclear. Although social media use may contribute to negative mental health outcomes, it is also possible that individuals who are already experiencing symptoms of anxiety and loneliness may use social media as a coping mechanism or to seek social support. Therefore, more research is needed to understand the complex relationship between social media use and mental health outcomes. It can also be concluded that the use of technology-based interventions can provide increased accessibility and convenience, anonymity and privacy, customizable and tailored support, cost-effectiveness, increased awareness and education, and reduced stigma. These findings demonstrate the potential of technology to offer effective and accessible mental health support for individuals in need.

The implications of investigating the relationship between social media usage and students' social anxiety, loneliness, and well-being within the context of digital mindfulness-based intervention are multifaceted. Firstly, as social media becomes increasingly integrated into students' lives, the study underscores the significance of understanding its potential repercussions on mental health. The findings can offer valuable insights to educational institutions, mental health professionals, and policymakers, prompting them to recognize the importance of promoting responsible social media usage among students. Secondly, the study's exploration of the effectiveness of digital mindfulness-based interventions in alleviating social anxiety, loneliness, and enhancing well-being holds significant implications for mental health intervention strategies. If proven efficacious, these interventions could serve as a practical and accessible means of addressing the psychological challenges posed by social media usage. This could potentially guide the development of tailored programs aimed at improving students' mental health and emotional resilience in the digital age. Furthermore, the study's focus on digital mindfulness-based interventions acknowledges the evolving nature of psychological interventions in the digital era. The implications of successful intervention highlight the potential of technology-assisted approaches to bridge the gap between traditional therapeutic methods and the modern digital landscape. This insight could inspire further innovation in mental health care, encouraging the integration of technology to reach wider audiences and promote positive mental well-being [ 51 ].

The current study also provides evidence that the intervention was effective in improving mental health outcomes over time. However, the study design does not allow us to determine the specific mechanisms by which the intervention was effective. Therefore, more research is needed to better understand how interventions can be optimized to improve mental health outcomes. Finally, while technology-based interventions can provide benefits such as convenience and accessibility, concerns about the quality and accuracy of mental health information available online, the lack of personal connection compared to traditional face-to-face therapy, and technical difficulties with accessing support have been reported by participants in this study.

Availability of data and materials

The data will be made available upon request from the author ( email: [email protected]).

Andersson G. Internet-delivered psychological treatments. Annu Rev Clin Psychol. 2016;12:157–79. https://doi.org/10.1146/annurev-clinpsy-021815-093006 .

Article   PubMed   Google Scholar  

Khoury B, Lecomte T, Fortin G, Masse M, Therien P, Bouchard V, Chapleau MA, Paquin K, Hofmann SG. Mindfulness-based therapy: a comprehensive meta-analysis. Clin Psychol Rev. 2013;33(6):763–71. https://doi.org/10.1016/j.cpr.2013.05.005 .

Kross E, Verduyn P, Demiralp E, Park J, Lee DS, Lin N, Shablack H, Jonides J, Ybarra O. Facebook use predicts declines in subjective well-being in young adults. PLoS ONE. 2013;8(8):e69841. https://doi.org/10.1371/journal.pone.0069841 .

Article   PubMed   PubMed Central   Google Scholar  

Twenge JM, Joiner TE, Rogers ML, Martin GN. Increases in depressive symptoms, suicide-related outcomes, and Suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clin Psychol Sci. 2018;6:3–17. https://doi.org/10.1177/2167702617723376 .

Article   Google Scholar  

Boot WR, Kramer AF, Simons DJ, Fabiani M, Gratton G. The effects of video game playing on attention, memory, and executive control. Acta Psychol (Amst). 2008;129(3):387–98. https://doi.org/10.1016/j.actpsy.2008.09 .

Green CS, Bavelier D. Learning, attentional control, and action video games. Curr Biol. 2012;22(6):R197–206. https://doi.org/10.1016/j.cub.2012.02.012 .

Anderson CA, Shibuya A, Ihori N, Swing EL, Bushman BJ, Sakamoto A, Rothstein HR, Saleem M. Violent video game effects on aggression, empathy, and prosocial behavior in eastern and western countries: a meta-analytic review. Psychol Bull. 2010;136(2):151–73. https://doi.org/10.1037/a0018251 .

Gentile DA, Anderson CA, Yukawa S, Ihori N, Saleem M, Ming LK, Shibuya A, Liau AK, Khoo A, Bushman BJ, Rowell Huesmann L, Sakamoto A. The effects of prosocial video games on prosocial behaviors: international evidence from correlational, longitudinal, and experimental studies. Pers Soc Psychol Bull. 2009;35(6):752–63. https://doi.org/10.1177/0146167209333045 .

Tran S, Smith L, El-Den S, Carter S. The Use of Gamification and incentives in Mobile Health apps to improve Medication Adherence: scoping review. JMIR Mhealth Uhealth. 2022;10(2):e30671. https://doi.org/10.2196/30671 .

Wahbeh H, Goodrich E, Goy E, Oken BS. Mechanistic pathways of Mindfulness Meditation in Combat Veterans with Posttraumatic stress disorder. J Clin Psychol. 2016;72(4):365–83. https://doi.org/10.1002/jclp.22255 .

Lai F, Wang L, Zhang J, Shan S, Chen J, Tian L. Relationship between Social Media Use and social anxiety in College students: Mediation Effect of Communication Capacity. Int J Environ Res Public Health. 2023;20(4):3657. https://doi.org/10.3390/ijerph20043657 .

Seabrook EM, Kern ML, Rickard NS, Social Networking Team. (2016). Social networking sites, depression, and anxiety: a systematic review. JMIR Mental Health, 3(4), e50.

Zhang M, Sun X, Qin X, et al. Problematic utilization of online social networking site in Chinese college students: prediction of personality and dynamic mediators. Curr Psychol. 2022. https://doi.org/10.1007/s12144-022-03150-7 .

Tandoc EC Jr, Ferrucci P, Duffy M. Facebook use, envy, and depression among college students: Is facebooking depressing? Comput Hum Behav. 2015;43:139–46.

Ulvi O, Karamehic-Muratovic A, Baghbanzadeh M, Bashir A, Smith J, Haque U. Social Media Use and Mental Health: A Global Analysis. Epidemiologia (Basel). 2022;3(1):11–25. https://doi.org/10.3390/epidemiologia3010002 .

Schønning V, Hjetland GJ, Aarø LE, Skogen JC. Social Media Use and Mental Health and Well-Being Among Adolescents - A Scoping Review. Front Psychol. 2020;14(11):1949. https://doi.org/10.3389/fpsyg.2020.01949 .

Sharma MK, John N, Sahu M. Influence of social media on mental health: a systematic review. Curr Opin Psychiatry. 2020;33(5):467–75. https://doi.org/10.1097/YCO.0000000000000631 .

Köse ÖB, Doğan A. (2019). The Relationship between Social Media Addiction and Self-Esteem among Turkish University Students. Addicta: The Turkish journal on addictions, 6(1).

Błachnio A, Przepiorka A, Senol-Durak E, Durak M, Sherstyuk L. The role of personality traits in Facebook and Internet addictions: a study on Polish, Turkish, and Ukrainian samples. Comput Hum Behav. 2017;68:269–75.

Chen B, Vansteenkiste M, Beyers W, Boone L, Deci EL, Van der Kaap-Deeder J, Verstuyf J. Basic psychological need satisfaction, need frustration, and need strength across four cultures. Motivation Emotion. 2015;39:216–36.

Seabrook EM, Kern ML, Rickard NS. (2016). Social networking sites, depression, and anxiety: a systematic review. JMIR Mental Health, 3(4), e5842.

Linardon J, Cuijpers P, Carlbring P, Messer M, Fuller-Tyszkiewicz M. The efficacy of app‐supported smartphone interventions for mental health problems: a meta‐analysis of randomized controlled trials. World Psychiatr. 2019;18(3):325–36.

Strauss C, Gu J, Montero-Marin J, Whittington A, Chapman C, Kuyken W. Reducing stress and promoting well-being in healthcare workers using mindfulness-based cognitive therapy for life. Int J Clin Health Psychol. 2021;21(2):100227.

Lomas T, Medina JC, Ivtzan I, Rupprecht S, Eiroa-Orosa FJ. A systematic review of the impact of mindfulness on the well‐being of healthcare professionals. J Clin Psychol. 2018;74(3):319–55.

Valinskas S, Nakrys M, Aleknavicius K, Jonusas J. Sensa Mobile App for Managing Stress, Anxiety, and Depression Symptoms: Pilot Cohort Study. JMIR Form Res. 2023;13(7): e40671. https://doi.org/10.2196/40671 .

Biegel GM, Brown KW, Shapiro SL, Schubert CM. Mindfulness-based stress reduction for the treatment of adolescent psychiatric outpatients: a randomized clinical trial. J Consult Clin Psychol. 2009;77(5):855–66. https://doi.org/10.1037/a0016241 .

Birnbaum ML, Ernala SK, Rizvi AF, Arenare E, Van Meter R, De Choudhury A, Kane M. Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook. NPJ Schizophr. 2019;5(1):17.

Fuller-Tyszkiewicz M, Richardson B, Little K, Teague S, Hartley-Clark L, Capic T, and Hutchinson, D. Efficacy of a smartphone app intervention for reducing caregiver stress: randomized controlled trial. JMIR mental health. 2020;7(7):e17541.

Rathbone AL, Prescott J. The use of mobile apps and SMS messaging as physical and Mental Health Interventions: systematic review. J Med Internet Res. 2017;19(8):e295. https://doi.org/10.2196/jmir.7740 .

Kim S, Kim E. The use of virtual reality in Psychiatry: a review. Soa Chongsonyon Chongsin Uihak. 2020;31(1):26–32. https://doi.org/10.5765/jkacap.190037 .

Deady M, Johnston D, Milne D, Glozier N, Peters D, Calvo R, Harvey S. Preliminary effectiveness of a smartphone app to reduce depressive symptoms in the workplace: feasibility and acceptability study. JMIR Mhealth Uhealth. 2018;6(12):e11661.

Kern A, Hong V, Song J, Lipson SK, Eisenberg D. Mental health apps in a college setting: openness, usage, and attitudes. Mhealth. 2018;4:20. https://doi.org/10.21037/mhealth.2018.06.01 .

Free C, Phillips G, Galli L, et al. The effectiveness of mobile-health technology-based health behavior change or Disease management interventions for health care consumers: a systematic review. PLoS Med. 2013;10:e1001362. https://doi.org/10.1371/journal.pmed.1001362 .

Brindal E, Kakoschke N, Golley S, Rebuli M, Baird D. Effectiveness and feasibility of a self-guided Mobile App Targeting Emotional Well-being in healthy adults: 4-Week randomized controlled trial. JMIR Ment Health. 2023;10:e44925. https://doi.org/10.2196/44925 .

Karyotaki E, Klein AM, Riper H, Wit L, Krijnen L, Bol E, Bolinski F, et al. Examining the effectiveness of a web-based intervention for symptoms of depression and anxiety in college students: study protocol of a randomised controlled trial. BMJ Open. 2019;9(5):e028739. https://doi.org/10.1136/bmjopen-2018-028739 .

Alam N, Ahmed O, Naher L, Hiramoni FA. The psychometric properties of social anxiety scale for adolescents (SAS-A) short form-bangla. Heliyon. 2021;7(8):e07801. https://doi.org/10.1016/j.heliyon.2021.e07801 .

Stewart-Brown S, Tennant A, Tennant R, Platt S, Parkinson J, Weich S. (2009). Short Warwick–Edinburgh Mental Well-being Scale (SWEMWBS).

Jenkins-Guarnieri MA, Wright SL, Johnson B. Development and validation of a social media use integration scale. Psychol Popular Media Cult. 2013;2(1):38–50.

Garett R, Liu S, Young SD. The Relationship between Social Media Use and Sleep Quality among undergraduate students. Inf Commun Soc. 2018;21(2):163–73. https://doi.org/10.1080/1369118X.2016.1266374 .

Chou HT, Edge N. They are happier and having better lives than I am: the impact of using Facebook on perceptions of others’ lives. Cyberpsychol Behav Soc Netw. 2012;15(2):117–21. https://doi.org/10.1089/cyber.2011.0324 .

Carpenter JK, Andrews LA, Witcraft SM, Powers MB, Smits JA, Hofmann SG. Cognitive behavioral therapy for anxiety and related disorders: a meta-analysis of randomized placebo‐controlled trials. Depress Anxiety. 2018;35(6):502–14.

Zhang H, Zhang A, Liu C, et al. A brief online mindfulness-based group intervention for psychological distress among Chinese residents during COVID-19: a pilot randomized controlled trial. Mindfulness. 2021;12:1502–12. https://doi.org/10.1007/s12671-021-01618-4 .

Sucala M, Cuijpers P, Muench F, Cardoș R, Soflau R, Dobrean A, Achimas-Cadariu P, David D. Anxiety: there is an app for that. a systematic review of anxiety apps. Depress Anxiety. 2017;34(6):518–25. https://doi.org/10.1002/da.22654 .

Saddichha S, Al-Desouki M, Lamia A, Linden IA, Krausz M. Online interventions for depression and anxiety - a systematic review. Health Psychol Behav Med. 2014;2(1):841–81. https://doi.org/10.1080/21642850.2014.945934 .

Andersson G, Cuijpers P. Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cogn Behav Ther. 2009;38(4):196–205. https://doi.org/10.1080/16506070903318960 .

Proudfoot J, Klein B, Barak A, Carlbring P, Cuijpers P, Lange A, Ritterband L, Andersson G. Establishing guidelines for executing and reporting internet intervention research. Cogn Behav Ther. 2011;40(2):82–97. https://doi.org/10.1080/16506073.2011.573807 .

Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet. 2020;395(10223):470–3. https://doi.org/10.1016/S0140-6736(20)30185-9 .

Eysenbach G. (2011). Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact. J Med Internet Res, 13(4), e123.

Griffiths KM, Christensen H, Jorm AF. Predictors of depression stigma. BMC Psychiatr. 2008;8:25. https://doi.org/10.1186/1471-244X-8-25 .

Andersson G, Titov N, Dear BF, Rozental A, Carlbring P. Internet-delivered psychological treatments: from innovation to implementation. World Psychiatr. 2019;18(1):20–8. https://doi.org/10.1002/wps.20610 .

Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gen Hosp Psychiatr. 2013;35(4):332–8.

Download references

Acknowledgements

The authors would like to thank all participants.

Phased Results of Key Projects of Vocational Education and Teaching Reform in Henan Province in 2021 (Project No.: Yujiao [2021] 57946).

Author information

Authors and affiliations.

School of Marxism, Zhoukou Vocational and Technical College, Zhoukou, 466000, China

You can also search for this author in PubMed   Google Scholar

Contributions

Li Sun designed the study. Li Sun collected the data. Li Sun analyzed and interpreted the data, drafted the manuscript, proofread the paper, and verified the submitted version.

Corresponding author

Correspondence to Li Sun .

Ethics declarations

Ethics approval and consent to participate.

The ethical approval committee of Zhoukou Vocational and Technical College approved this study and issued a letter (No. 2023.2623), indicating the study has no side effects on the participants of the study. All experiments were performed in accordance with relevant guidelines and regulations. All methods were carried out in accordance with relevant guidelines and regulations.

Informed consent was obtained from all subjects.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

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

Supplementary Information

Additional file 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Sun, L. Social media usage and students’ social anxiety, loneliness and well-being: does digital mindfulness-based intervention effectively work?. BMC Psychol 11 , 362 (2023). https://doi.org/10.1186/s40359-023-01398-7

Download citation

Received : 29 August 2023

Accepted : 16 October 2023

Published : 31 October 2023

DOI : https://doi.org/10.1186/s40359-023-01398-7

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Mindfulness
• Social anxiety
• Social media usage

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

CONCEPTUAL ANALYSIS article

The effect of social media on the development of students’ affective variables.

• 1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China
• 2 School of Marxism, Hohai University, Nanjing, Jiangsu, China
• 3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

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

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd.

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

Publisher’s note

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.

Aalbers, G., McNally, R. J., Heeren, A., de Wit, S., and Fried, E. I. (2018). Social media and depression symptoms: A network perspective. J. Exp. Psychol. Gen. 148, 1454–1462. doi: 10.1037/xge0000528

PubMed Abstract | CrossRef Full Text | Google Scholar

Abbott, J. (2017). Introduction: Assessing the social and political impact of the internet and new social media in Asia. J. Contemp. Asia 43, 579–590. doi: 10.1080/00472336.2013.785698

CrossRef Full Text | Google Scholar

Alahmar, A. T. (2016). The impact of social media on the academic performance of second year medical students at College of Medicine, University of Babylon, Iraq. J. Med. Allied Sci. 6, 77–83. doi: 10.5455/jmas.236927

Banjanin, N., Banjanin, N., Dimitrijevic, I., and Pantic, I. (2015). Relationship between internet use and depression: Focus on physiological mood oscillations, social networking and online addictive behavior. Comp. Hum. Behav. 43, 308–312. doi: 10.1016/j.chb.2014.11.013

Barry, C. T., Sidoti, C. L., Briggs, S. M., Reiter, S. R., and Lindsey, R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. J. Adolesc. 61, 1–11. doi: 10.1016/j.adolescence.2017.08.005

Chang, Y. (2012). The relationship between maladaptive perfectionism with burnout: Testing mediating effect of emotion-focused coping. Pers. Individ. Differ. 53, 635–639. doi: 10.1016/j.paid.2012.05.002

Charoensukmongkol, P. (2018). The impact of social media on social comparison and envy in teenagers: The moderating role of the parent comparing children and in-group competition among friends. J. Child Fam. Stud. 27, 69–79. doi: 10.1007/s10826-017-0872-8

Chukwuere, J. E., and Chukwuere, P. C. (2017). The impact of social media on social lifestyle: A case study of university female students. Gender Behav. 15, 9966–9981.

Google Scholar

Drouin, M., Reining, L., Flanagan, M., Carpenter, M., and Toscos, T. (2018). College students in distress: Can social media be a source of social support? Coll. Stud. J. 52, 494–504.

Dumitrache, S. D., Mitrofan, L., and Petrov, Z. (2012). Self-image and depressive tendencies among adolescent Facebook users. Rev. Psihol. 58, 285–295.

PubMed Abstract | Google Scholar

Fernyhough, C. (2008). Getting Vygotskian about theory of mind: Mediation, dialogue, and the development of social understanding. Dev. Rev. 28, 225–262. doi: 10.1016/j.dr.2007.03.001

Festinger, L. (1954). A Theory of social comparison processes. Hum. Relat. 7, 117–140. doi: 10.1177/001872675400700202

Fleck, J., and Johnson-Migalski, L. (2015). The impact of social media on personal and professional lives: An Adlerian perspective. J. Individ. Psychol. 71, 135–142. doi: 10.1353/jip.2015.0013

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am. Psychol. 56, 218–226. doi: 10.1037/0003-066X.56.3.218

Frison, E., and Eggermont, S. (2016). Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood. Soc. Sci. Compu. Rev. 34, 153–171. doi: 10.1177/0894439314567449

Hanprathet, N., Manwong, M., Khumsri, J., Yingyeun, R., and Phanasathit, M. (2015). Facebook addiction and its relationship with mental health among Thai high school students. J. Med. Assoc. Thailand 98, S81–S90.

Hiver, P., and Al-Hoorie, A. H. (2019). Research Methods for Complexity Theory in Applied Linguistics. Bristol: Multilingual Matters. doi: 10.21832/HIVER5747

Iwamoto, D., and Chun, H. (2020). The emotional impact of social media in higher education. Int. J. High. Educ. 9, 239–247. doi: 10.5430/ijhe.v9n2p239

Keles, B., McCrae, N., and Grealish, A. (2020). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolesc. Youth 25, 79–93. doi: 10.1080/02673843.2019.1590851

Ley, B., Ogonowski, C., Hess, J., Reichling, T., Wan, L., and Wulf, V. (2014). Impacts of new technologies on media usage and social behavior in domestic environments. Behav. Inform. Technol. 33, 815–828. doi: 10.1080/0144929X.2013.832383

Li, J.-B., Lau, J. T. F., Mo, P. K. H., Su, X.-F., Tang, J., Qin, Z.-G., et al. (2017). Insomnia partially mediated the association between problematic Internet use and depression among secondary school students in China. J. Behav. Addict. 6, 554–563. doi: 10.1556/2006.6.2017.085

Mathewson, M. (2020). The impact of social media usage on students’ mental health. J. Stud. Affairs 29, 146–160.

Neira, B. C. J., and Barber, B. L. (2014). Social networking site use: Linked to adolescents’ social self-concept, self-esteem, and depressed mood. Aus. J. Psychol. 66, 56–64. doi: 10.1111/ajpy.12034

O’Dea, B., and Campbell, A. (2011). Online social networking amongst teens: Friend or foe? Ann. Rev. CyberTher. Telemed. 9, 108–112.

Radovic, A., Gmelin, T., Stein, B. D., and Miller, E. (2017). Depressed adolescents positive and negative use of social media. J. Adolesc. 55, 5–15. doi: 10.1016/j.adolescence.2016.12.002

Sampasa-Kanyinga, H., and Lewis, R. F. (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychol. Behav. Soc. Network. 18, 380–385. doi: 10.1089/cyber.2015.0055

Sriwilai, K., and Charoensukmongkol, P. (2016). Face it, don’t Facebook it: Impacts of social media addiction on mindfulness, coping strategies and the consequence on emotional exhaustion. Stress Health 32, 427–434. doi: 10.1002/smi.2637

Stapel, D. A. (2007). “In the mind of the beholder: The interpretation comparison model of accessibility effects,” in Assimilation and Contrast in Social Psychology , eds D. A. Stapel and J. Suls (London: Psychology Press), 143–164.

Stapel, D. A., and Koomen, W. (2000). Distinctiveness of others, mutability of selves: Their impact on self-evaluations. J. Pers. Soc. Psychol. 79, 1068–1087. doi: 10.1037//0022-3514.79.6.1068

Tang, F., Wang, X., and Norman, C. S. (2013). An investigation of the impact of media capabilities and extraversion on social presence and user satisfaction. Behav. Inform. Technol. 32, 1060–1073. doi: 10.1080/0144929X.2013.830335

Tsitsika, A. K., Tzavela, E. C., Janikian, M., Ólafsson, K., Iordache, A., Schoenmakers, T. M., et al. (2014). Online social networking in adolescence: Patterns of use in six European countries and links with psychosocial functioning. J. Adolesc. Health 55, 141–147. doi: 10.1016/j.jadohealth.2013.11.010

Vernon, L., Modecki, K. L., and Barber, B. L. (2017). Tracking effects of problematic social networking on adolescent psychopathology: The mediating role of sleep disruptions. J. Clin. Child Adolesc. Psychol. 46, 269–283. doi: 10.1080/15374416.2016.1188702

Virden, A., Trujillo, A., and Predeger, E. (2014). Young adult females’ perceptions of high-risk social media behaviors: A focus-group approach. J. Commun. Health Nurs. 31, 133–144. doi: 10.1080/07370016.2014.926677

Wang, P., Wang, X., Wu, Y., Xie, X., Wang, X., Zhao, F., et al. (2018). Social networking sites addiction and adolescent depression: A moderated mediation model of rumination and self-esteem. Pers. Individ. Differ. 127, 162–167. doi: 10.1016/j.paid.2018.02.008

Weng, L., and Menczer, F. (2015). Topicality and impact in social media: Diverse messages, focused messengers. PLoS One 10:e0118410. doi: 10.1371/journal.pone.0118410

Yan, H., Zhang, R., Oniffrey, T. M., Chen, G., Wang, Y., Wu, Y., et al. (2017). Associations among screen time and unhealthy behaviors, academic performance, and well-being in Chinese adolescents. Int. J. Environ. Res. Public Health 14:596. doi: 10.3390/ijerph14060596

Zareen, N., Karim, N., and Khan, U. A. (2016). Psycho-emotional impact of social media emojis. ISRA Med. J. 8, 257–262.

Zhang, R. (2017). The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, social support, and mental health among college students. Comp. Hum. Behav. 75, 527–537. doi: 10.1016/j.chb.2017.05.043

Keywords : affective variables, education, emotions, social media, post-pandemic, emotional needs

Citation: Chen M and Xiao X (2022) The effect of social media on the development of students’ affective variables. Front. Psychol. 13:1010766. doi: 10.3389/fpsyg.2022.1010766

Received: 03 August 2022; Accepted: 25 August 2022; Published: 15 September 2022.

Reviewed by:

Copyright © 2022 Chen and Xiao. 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: Miao Chen, [email protected] ; Xin Xiao, [email protected]

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.

An official website of the United States government

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

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

• Publications
• Account settings

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

• Advanced Search
• Journal List
• JMIR Form Res
• PMC10337317

Exploring the Impact of Social Media on Anxiety Among University Students in the United Kingdom: Qualitative Study

1 Faculty of Medicine, Imperial College London, London, United Kingdom

Rafey Omar Asif

Arunima basu, dylan kanapathipillai, haadi salam.

2 Faculty of Life Sciences and Medicine, Kings College London, London, United Kingdom

Rania Selim

Jahed zaman, andreas benedikt eisingerich.

3 Imperial College Business School, Imperial College London, London, United Kingdom

Associated Data

Systematic literature review.

Thematic analysis.

The data sets, including the second-order themes, subthemes, and first-order codes generated during or analyzed during this study, are available from the corresponding author upon request.

The rapid surge in social media platforms has significant implications for users’ mental health, particularly anxiety. In the case of social media, the impact on mental well-being has been highlighted by multiple stakeholders as a cause for concern. However, there has been limited research into how the association between social media and anxiety arises, specifically among university students—the generation that has seen the introduction and evolution of social media, and currently lives through the medium. Extant systematic literature reviews within this area of research have not yet focused on university students or anxiety, rather predominantly investigating adolescents or generalized mental health symptoms and disorders. Furthermore, there is little to no qualitative data exploring the association between social media and anxiety among university students.

The purpose of this study is to conduct a systematic literature review of the existing literature and a qualitative study that aims to develop foundational knowledge around the association of social media and anxiety among university students and enhance extant knowledge and theory.

A total of 29 semistructured interviews were conducted, comprising 19 male students (65.5%) and 10 female students (34.5%) with a mean age of 21.5 years. All students were undergraduates from 6 universities across the United Kingdom, with most students studying in London (89.7%). Participants were enrolled through a homogenous purposive sampling technique via social media channels, word of mouth, and university faculties. Recruitment was suspended at the point of data saturation. Participants were eligible for the study if they were university students in the United Kingdom and users of social media.

Thematic analysis resulted in 8 second-order themes: 3 mediating factors that decrease anxiety levels and 5 factors that increase anxiety levels. Social media decreased anxiety through positive experiences, social connectivity, and escapism. Social media increased anxiety through stress, comparison, fear of missing out, negative experiences, and procrastination.

Conclusions

This qualitative study sheds critical light on how university students perceive how social media affects their anxiety levels. Students revealed that social media did impact their anxiety levels and considered it an important factor in their mental health. Thus, it is essential to educate stakeholders, including students, university counselors, and health care professionals, about the potential impact of social media on students’ anxiety levels. Since anxiety is a multifactorial condition, pinpointing the main stressors in a person’s life, such as social media use, may help manage these patients more effectively. The current research highlights that there are also many benefits to social media, and uncovering these may help in producing more holistic management plans for anxiety, reflective of the students’ social media usage.

Introduction

The rapid surge in social media platforms and users has substantial implications for mental health, particularly anxiety. In the case of social media, the impact on mental well-being has been highlighted by multiple stakeholders as a cause for concern, even being spotlighted by the government of the United Kingdom in the latest Mental Health and Well-being Discussion Paper [ 1 ]. However, there has been a lack of research into how the association between social media and anxiety arises, specifically among university students, a cohort that has witnessed the inception and progression of social media and predominantly engages with it.

Social media refers to a group of internet-based applications that build on the foundations of Web 2.0, allowing the creation and exchange of user-generated content [ 2 ]. There are currently 4.2 billion active social media users worldwide [ 3 ]. Global social media usage is projected to rise by 16.7% over the next 5 years, indicating it is here to stay [ 3 ]. Extant work notes social media’s critical role in people’s lives, including people becoming attached to their favorite social media app and experiencing separation distress when not able to use it [ 4 - 7 ]. Social media has also been highlighted as a potential force for good, for example, through the use of gamification, enhancing people’s happiness, and helping people quit smoking [ 8 - 13 ]. Important questions, however, remain regarding the impact of social media on university students. Consequently, its implications, both positive and negative, ought to be investigated, especially with regard to university students, of whom 76% have an account on some form of social media platform [ 14 ].

Behaviors stemming from social media use, including impaired sleep and sedentary practices, have been suggested as possible mechanisms for the rise of anxiety and symptoms of other mental health disorders [ 15 ]. University students, in particular, may fall victim to such behaviors, with it being found that up to 60% of all college students experience poor sleep quality [ 16 ]. Anxiety, defined by the National Health Service (NHS) [ 17 ] as a feeling of unease, such as worry or fear, is commonplace in adults in the 21st century, with anxiety disorders being the most prevalent form of mental illness [ 18 ]. Anxiety disorders in particular refer to general anxiety disorder, panic disorder with or without agoraphobia, social anxiety disorder, specific phobias, and separation anxiety disorder [ 19 ]. The importance of understanding the impact of social media on anxiety in university students is a priority now more than ever due to the increasing prevalence of both social media use and anxiety [ 3 ]. Studies within child and adolescent populations have demonstrated a significant association between social media use and anxiety [ 15 , 20 ]. However, this association is complicated by the various pathways outlined in much of the adolescent literature. One study described how different metrics of social media activity, including the user’s number of social media accounts and frequency of social media checking, were significantly correlated with higher levels of anxiety [ 21 ]. Another study demonstrated the implication of sleep within this relationship: increased nighttime-specific social media use resulted in later bedtimes and poor sleep, which ultimately led to increased anxiety [ 20 ]. Other phenomena have also been implicated within this relationship, including time spent, addiction, and emotional investment [ 15 , 22 ].

While this relationship has been well researched within child and adolescent populations, there has been limited focus on other groups, such as university students. Adolescents are at a unique stage of development and vulnerable to environmental insults such as social media use due to increased central nervous system plasticity, biological changes, and the shaping of psychological mechanisms [ 23 ]. Yet, university students are the population most susceptible to mental health disorders, with anxiety prevalence higher than the general population average at 11.2% [ 24 - 27 ]. Researchers often consider university students as a distinct population, also with their own unique set of risk factors that have been proposed as contributing toward the anxiety reported. These include strenuous academic demands, challenges of leaving home, distance from support networks, and a harmful predisposition to substance abuse, all specific to this cohort [ 28 , 29 ]. These significant differences in environmental context may affect the interactions these groups have with social media and the resultant impact on anxiety, thus limiting the generalizability of existing adolescent population findings within university student cohorts. Therefore, to build on the limited literature focused on the university student population, a systematic literature review (SLR) on the impact of social media on anxiety was conducted.

Systematic Literature Review

The protocol for the systematic review was registered with the International Prospective Register of Systematic Reviews (Prospero; CRD42022304959) and conducted in accordance with guidelines from the PRISMA (Preferred Reporting of Items for Systematic Reviews and Meta-Analyses) statement [ 30 ]. A systematic search of the literature was conducted in 3 electronic databases: OVID, MEDLINE, Embase, and PsychINFO, from the inception of the databases up to December 13, 2021, as shown in Figure 1 . Search terms were identified prior to the search to generate the search strategy ( Multimedia Appendix 1 [ 31 - 53 ]). A total of 23 studies ( Multimedia Appendix 1 ) were included for analysis within this review from the 1110 available papers following abstract and full-text screenings according to the inclusion and exclusion criteria by 3 independent researchers ( Multimedia Appendix 1 ). For each included study, researchers extracted the author names, country of origin, study design, sample cohort, sample demographic data, relevant exposure measures, relevant outcome measures, and main findings ( Multimedia Appendix 1 ). Owing to the diverse characteristics of the selected studies (ie, study designs, intervention types, or outcomes), a narrative synthesis approach was adopted for a more transparent presentation of findings.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.

Principal Findings

This systematic review explored the current literature on the association between social media use and anxiety among university students, and it was evident that the association is multifaceted. The included studies were generally of fair to good quality, thus suggesting reasonable validity in our findings.

This review identified that the most commonly investigated factor impacting levels of anxiety was the frequency of social media use, with contrasting associations across the studies [ 31 - 35 ]. Only 1 paper demonstrated a significantly positive association, with increased Facebook use leading to higher anxiety levels [ 31 ]. The remaining studies, however, highlighted significant relationships across other components of social media use beyond the frequency of use.

The nature of social media activity that university students engaged in was a more significant component in evoking anxiety. It was found that the viewing of certain image types induced higher levels of anxiety. Specifically, anxiety levels were immediately higher following the exposure of students to thin-ideal images, with anxiety-reducing as time elapsed [ 36 ]. Another study shared similar findings, with anxiety increasing following activity involving exposure to appearance-focused Instagram accounts [ 37 ]. This may be explained by social comparison theory, whereby comparison of one’s body to others may threaten one’s self-evaluation leading to feelings of anxiety [ 54 ]. Interestingly, while this behavior is more commonly observed in adolescents, our findings suggest that this phenomenon is present in university students as well [ 55 , 56 ]. This is further supported by another study identifying envy as a potential mediator between the relationship between social media and anxiety [ 38 ].

Mediators are indirect pathways through which social media use impacts anxiety levels. Further to envy, another key mediator identified as stress, with social media use increasing stress levels and, consequently, anxiety levels. The positive association between stress and anxiety levels has previously been highlighted in prior literature [ 56 , 57 ], with potential biochemical changes in the body providing an explanation for this association. The role of social media in exacerbating stress has also been previously identified within adolescents [ 58 ] through feeling overwhelmed as well as through indirect ways such as impacting sleep [ 59 ]. Psychological capital was another mediator identified, referring to a combination of positive psychological constructs including hope, resilience, optimism, and self-efficacy [ 60 ]. Social media use had a significant negative effect on psychological capital, while psychological capital decreased anxiety levels. This finding may be particularly useful in attempting to moderate anxiety levels in this population.

Additionally, a few experimental studies showed how social media abstinence can be beneficial in reducing anxiety levels, which is an important consideration in identifying techniques to aid those that experience increased anxiety levels as a result of social media use [ 39 , 40 ].

Conclusion and Design of the Primary Study

The SLR demonstrated that various elements and characteristics of social media can impact anxiety beyond the frequency of use. The SLR further identified a significant lack of qualitative studies within this field of research, highlighting the inadequate depth of exploration on the subject. Further studies are needed to address the underlying pathways and mechanisms through which social media impacts anxiety levels within this population. Additionally, there was a distinct lack of studies that investigated this association in the United Kingdom.

Aims and Objectives

We conducted a qualitative study to examine the impact of social media on university student’s anxiety levels through qualitative research.

Primary data collection was carried out through a mono-method qualitative study approach, in line with an interpretive and inductive approach to underpin and reinforce existing theory and carefully navigate the complex and subjective nature of the interactions between social media and anxiety perceptions [ 61 ]. This study was conducted as per ethical approval by the Imperial College Research Ethics Committee, following assessment by the Research Governance and Integrity Team and Head of Department on February 8, 2022 (ICREC reference number: 21IC7395).

Setting and Sample

A total of 29 semistructured interviews were conducted, comprising 19 male students (65.5%) and 10 female students (34.5%) with a mean age of 21.5 years. All students were undergraduates from 6 universities across the United Kingdom, with most students studying in London (89.7%). Participants were also asked to self-report their ethnicity using the UK Office for National Statistics ethnicity categories. Participants were enrolled through a homogenous purposive sampling technique via social media channels, word of mouth, and across different university faculties in different regions of the United Kingdom. Recruitment was suspended at the point of data saturation. Participants were eligible for the study if they were university students in the United Kingdom and users of social media.

Data Collection

An exploratory pilot interview was conducted prior to data collection, allowing for analysis regarding length, style of question, and interviewer mannerisms to ensure consistency amongst interviewers [ 62 - 64 ].

The semistructured interview guide ( Textbox 1 ) was informed by the aims and objectives of the project, the systematic review findings, and the pilot interview learnings. The interview guide focused on eliciting views and feelings involving (1) the participants’ current social media use, (2) the participants’ general opinions on social media, and (3) the participants’ perceptions of the impact of social media on their feelings of anxiety.

Semistructured interview guide for face-to-face individual interviews.

• Describe how you use social media at the moment. (Clarify using probing questions below if necessary)
• Which social media platform do you use? 
• How much of each social media platform do you use each day? 
• When do you use social media the most?
• What level of activity do you have on social media (passive or active)? For example, passive would be you just scrolling through, and active would be where you are interacting with posts such as messaging, sharing, posting, commenting, and so on.
• What does your use of social media consist of?
• What type of content do you view using social media?
• What are your motivations for using social media?
• What are your views on social media?
• Has social media usage impacted your well-being? If so, how?
• Has social media usage impacted your mental health? If so, how?
• Has social media ever explicitly benefited you? If so, how?
• Has social media use explicitly upset you? If so, how?
• Has social media impacted your anxiety on a day-to-day basis? If so, how?
• Has social media ever impacted your anxiety symptoms? If so, how?
• Which social media platforms have the biggest impact on your anxiety? Are there any reasons for this?
• What type of content or media has the biggest impact on your anxiety?

Web-based interviews were conducted via Microsoft Teams and Zoom between March and April 2022. Interviews consisted of 2 interviewers: 1 principal interviewer and 1 scribe. Interviews lasted approximately 30-60 minutes and were transcribed.

Data Analysis

Thematic analysis of the semistructured interviews was conducted using Braun and Clarke’s 6 steps to provide a rich yet complex analysis of the data in a flexible and robust manner [ 61 , 65 ]. Gioia et al’s [ 66 ] approach was used to construct the data structure to ensure a rigorous inductive research approach. A concurrent and recursive analytical approach was used, in which researchers analyzed data during collection and coding to refine codes and search for themes. To achieve consensual validation of any discrepancies, 3 researchers carried out a thematic analysis.

Phase 1: Data Familiarization

Each investigator read all the transcripts independently, generating concepts, and capturing ideas that explain the phenomena of interest.

Phase 2: Generation of First-Order Codes

First-order codes were generated independently by each investigator to link data referring to the same specific meanings [ 61 ]. Researchers then discussed the first-order codes identified and combined similar ideas under a single code, conscientiously adhering to participants’ terms.

Phase 3: Generation of Second-Order Themes and Subthemes

Investigators sought out similarities and differences among the first-order codes, categorizing these into subthemes and then into second-order themes. To produce a vibrant inductive model, researchers acknowledged the interrelationships among emergent themes. This formed the data structure, which visually demonstrates the process by which raw data transforms into second-order themes.

Phases 4 and 5: Defining, Naming, and Review of Themes

Themes and their encompassing components were reviewed, refined, and reorganized in a 2-level process by the 3 researchers. First-order codes were reviewed to test for coherence and relevance in their overarching subtheme. The subthemes and second-order themes were then refined to ensure relevance to research aims and an accurate representation of the data. The entire data set was revisited, and additional data were recoded that may have been missed in earlier phases. The names of the themes were crafted to be clear and instructive.

Phase 6: Producing a Report

Findings were presented through an analytic narrative approach that included explanations for each theme accompanied by examples from participants to demonstrate the merit and validity of the analysis [ 65 , 66 ].

Research Trustworthiness

The 4 criteria by which Guba [ 67 ] establishes trustworthiness are credibility, transferability, dependability, and confirmability [ 68 ]. Credibility was ensured through member checking of transcriptions, whereby participants were provided with their interview transcripts to guarantee accuracy [ 61 ]. To establish transferability, demographic characteristics have been provided for all participants [ 61 , 68 ]. Dependability was achieved through auditing the methodology, as demonstrated by the pilot interview [ 69 ]. To ensure confirmability, this study’s methodology and rationale served as a rudimentary audit trail, and stages such as thematic analysis were conducted independently by multiple researchers.

Ethics Approval

Ethical approval was given by the Imperial College Research Ethics Committee (ICREC reference number: 21IC7395). This study followed the principles of the Helsinki Declaration and the Danish Code for Research Integrity. Informed consent was obtained verbally and in writing from all participants, and they had the right to withdraw from the study at any given point. The interviews conducted were private, and the study data was kept anonymous, including from other members of the research team. Additionally, participants did not receive any compensation for their participation.

A total of 29 student interviews were conducted; Table 1 represents a summary of the demographics of the participants interviewed.

Demographics of interview participants (N=29).

Student participants’ experiences and perceptions of social media use and its effect on anxiety were analyzed. The analysis resulted in 8 second-order themes: 3 factors that decrease anxiety levels and 6 factors that increase anxiety levels ( Textboxes 2 and 3 ). Factors perceived to be decreasing student anxiety levels were social connections, positive experiences, and escapism (themes 1 to 3, respectively). Factors that were perceived to increase student anxiety levels were comparison, fear of missing out (FOMO), procrastination, stress, and negative experience (themes 4 to 8, respectively), summarized.

Second-order themes and subthemes of factors decreasing student anxiety.

Social connections

• Connect with friends and family
• Communities
• Distraction

Positive experiences

• Positive validation
• Positive content

Second-order themes and subthemes of factors increasing student anxiety.

• Comparison of life
• Comparison of body type
• Fear of missing out
• Procrastination
• Feeling overwhelmed
• Overthinking interactions
• Disturbing and harmful content
• Cyberbullying
• Judgment by others

These second-order themes are further organized into subthemes, which are elaborated on in detail, providing quotes from participants for further harmonization and understanding. First-order concepts and selected quotes illustrate these themes (see Multimedia Appendix 2 ). We have replaced participant names with participant numbers (eg, Student 1).

Theme 1: Social Connections

Subtheme 1.1: connection with friends and family.

A majority of participants (n=19) spoke of the benefits of social media as a tool to create and maintain social connections. When students were worried about long-distance relationships, social media platforms allowed them to maintain these relationships, thus overcoming geographical barriers.

I’m a very social person so I like that side of social media. It helps me stay connected with people all around the world, even my cousins who live abroad Student 22

Social media provided students with a unique opportunity to grow their social circles by building and even reestablishing friendships:

I’ve met like some of my closest most dear friends through it Student 7

I lost contact with him for 10 plus years. We found each other through Instagram. So, we started messaging again. Student 12

Importantly, social media prevented student loneliness and social isolation during difficult times during their transition into university or during the COVID-19 pandemic when students were quarantined:

In terms of my mental well-being, it can help me stop loneliness and keep in touch with friends and family especially when I’m at university. Student 27

So even though you’re at home, by yourself, or with just your family, you’re actually like, connected with the world out there. Student 6

Subtheme 1.2: Web-Based Communities and Support

The establishment of connections through social media has offered opportunities for students to surround themselves with supportive web-based communities of like-minded people. These have formed sources of support and praise:

I’m fortunate enough to belong in one where everyone seeks to uplift one another, with the work you are producing being praised and promoted. Student 21

In lockdown, where a lot of people were then doing like the virtual calls and virtual iftars and virtual like, talks and things and it created such a nice community. Student 6

Theme 2: Positive Experiences Associated With Social Media Usage

Positive experiences are described as social media interactions that induce happiness and inspiration in students, especially when they are in a low mood. This encompassed viewing and interacting with positive content and receiving positive validation.

Subtheme 2.1: Positive Content

There were plentiful types of positive media consumed by students (n=10) on social media, including spiritual, creative, informational, and activist content. This motivated students to adopt healthier lifestyles and inspired creativity.

I can follow someone that has a healthy living account for example I find that seeing posts like that would encourage me to be better or to go out and go for a run or pick something healthy to do. Student 15

So in terms of creativity, it makes me happy seeing other people’s work and then using that to inspire myself. Student 17

Students revealed that social media enables greater visibility and support for social issues and activism. Through awareness and education on global movements through social media, students bonded with others and expressed solidarity.

I’ve learned a lot from people through social media, just different people’s mindsets and different people’s viewpoints. I think specially with certain campaigns, such as BLM [Black Lives Matter] for example, that really showed the power of people when they come together. Student 6

Another benefit mentioned by some students (n=3) was access to religious content, which induced a sense of hope and motivation.

Social media has benefited me a great deal in terms of coming closer to my own faith. I follow a lot of religious accounts on social media and they tend to show positive, bitesize clips that really provides me with hope and motivation to continue being strong in your faith. Student 27

Subtheme 2.2: Positive Validation

Some students mentioned that social media was a channel to receive positive validation and compliments (n=5). Students revealed that they were positively impacted by web-based exchanges with peers:

The endorphins that come with posting or the number of likes does have a positive impact on me Student 22

It’s nice getting positive feedback from people on stuff that you posted and it’s nice receiving compliments, but also complementing other people on stuff they’ve posted. Student 15

Theme 3: Escapism

Social media was described as providing students with an escape from mental health issues or any other stressors that may be plaguing their thoughts, thereby providing them with a sense of calm and relief.

Subtheme 3.1: Distraction

To mentally cope with and divert feelings of anxiety, students discussed using social media as an instrument to distract from or avoid their problems.

If there is anything in the real world that affects my anxiety, funnily social media can act as a way of helping me cope with it. It puts my mind in another place; a place where I don’t have to think too much Student 22

Pull me out of my like, state of like despair, into anything that’s stimulating. That’s why I was like, watching things that were just like, utter trash, like it made no sense. But like, it was something to just distract me. And that’s what I needed at that time. Student 4

Subtheme 3.2: Relief

Social media is commonly used for leisure and mindless entertainment to relax and reduce anxiety levels.

It’s also a nice break in the day so if you had a busy day at work you can just have a quick scroll through. Student 16

Sometimes if you’re just feeling a bit you know, as you put like, a bit anxious or maybe overwhelmed. Just doing something pretty mundane, can sort of help. Student 20

Theme 4: Comparison

A major contributor to anxiety in students (n=22) was described as the comparisons that they would draw between their own lives and the lives of others seen on social media, be that people that they know personally or other personalities such as influencers or celebrities.

Subtheme 4.1: Comparison of Life

Students specifically discussed the comparison of factors that were external to their own physical being. A common thread of comparison was that of the participants’ social lives, for example, seeing how many friends others had or the enjoyment that their counterparts attained from social gatherings. Seeing such content from peers would drive students to be anxious.

When people post a lot of content about their lives whilst you’re just at home, not doing anything special, you feel anxious in that you should really be doing something. Or that I am not doing enough Student 21

Students mentioned that they also compared their academic performances and other life achievements to others, saying that they felt anxious about not performing to the same standard:

...just looking at the success of others and comparing it to my lack of success really heightened my anxiety and pushed me down further. Student 27

One student mentioned the comparison of wealth and social status as a reason for anxiety:

So you can actually see who is financially more well off or of a higher social economic class and then you’re kind of comparing yourself to these people who have more money than you Student 10

Subtheme 4.2: Comparison of Body Types

Students felt that image-based social media platforms contributed to anxiety as they led them to compare their appearance to the perceived ideals that they saw on platforms.

I had like an issue with body image. It wasn’t a big thing, but it played on the back of my mind. And with social media like Instagram, where a big part of it is posting photos of yourself, I could definitely see why it would be a negative Student 11

I think it comes down to influencers – like they have good body types and things that can make you quite insecure. Student 25

Theme 5: FOMO

The concept of FOMO was cited by students as another noteworthy outcome of social media use that contributed to anxiety. FOMO consists of the feeling that by being away from social media, one could eventually feel anxious about missing out on messages.

I would not use my phone for even just 2 hours and I would feel like I was missing out on something. This would drive me crazy and add so much unnecessary stress Student 24

Additionally, in some cases (n=2), FOMO was mentioned to be occurring because of comparison to what other people posted on social media:

You see so many different stories of people doing different things. Sometimes you get that fear of missing out. Student 11

People around me were always on their phones using these apps, so naturally, I would do the same. If I wasn’t on it, it would bring the fear that I felt like I was missing out Student 24

Theme 6: Procrastination

Students (n=13) discussed how the nature of social media is such that they would constantly return to it and hence consume a lot of time. This impulsive use caused them to use social media, thereby procrastinating and causing anxiety from the feeling that they could have been more productive with their time.

if I have a deadline or things to do, like exam preparation, I am not one to just focus on that. I tend to procrastinate an unhealthy amount. Social media takes time away from me completing my uni work, doing my chores or doing things to better myself and, in turn, this increases my anxiety levels. Student 22

Theme 7: Stress

Students also mentioned (n=8) how social media was a large cause of stress through various mechanisms, which in turn caused them to be more anxious.

Subtheme 7.1: Feeling Overwhelmed

Excessive connectivity to others through social media would lead to students feeling overwhelmed by an unmanageable number of notifications. The perceived obligation to respond rapidly was what drove students to feel anxious.

With WhatsApp, there’s kind of a chronic stress and background, especially when you have like later messages and stuff coming through and you can kind of feel obliged to kind of just continually be online and be active Student 5

Subtheme 7.2: Overthinking Interactions

Constant contact with people through direct messages or posts on social media led to students overanalyzing the web-based interactions that they had. Students were self-conscious in their social media interactions, which made them anxious about how their peers might respond.

There is so much overthinking with it; thinking that maybe this person didn’t like me. Or perhaps you message someone, and you see that they are online, but they do not message you. Again, sometimes I look at this and think “Ok, does this person not have enough time for me?” Student 27

Theme 8: Negative Experiences

A common theme that arose from the interviews (n=18) was negative experiences on social media, which were described as those that cause displeasure and unhappiness in students. These negative experiences encompass the consumption of disturbing and harmful content, perceived judgment from others, and being a victim of cyberbullying.

Subtheme 8.1: Disturbing and Harmful Content

A striking factor that induced anxiety, as mentioned by students, was the consumption of distressing content. This was mentioned in multiple contexts; for example, viewing distressing media on the internet was said to cause negative emotional states, possibly even exacerbating symptoms in those already experiencing mental health disorders.

An example of such distressing content is the web-based promotion and glamorization of self-harm–related media.

I think it is scary to think that people can follow pages and accounts that may encourage them to self-harm or to commit suicide or to restrict eating or things like this Student 15

Subtheme 8.2: Judgment by Others

Social media encouraged student behaviors such that they would be anxious about being judged by peers they were connected with. This potential judgment led to students feeling anxious about potential disapproval by others.

I think it mostly lies in the fear of not knowing how it will be perceived by others. I don’t think it’s an innate thing. I think it’s not knowing how other people will react to it Student 18

Posting was an intensely anxious endeavor where students were concerned about the number of likes obtained, even considering removing a post if it did not perform well.

I think twice about keeping pictures up that didn’t receive as many likes as other posts of mine…it is embarrassing if you do not hit a certain number. It makes you feel less valued and less appreciated. Student 27

Subtheme 8.3: Cyberbullying

A considerable negative experience that students had on social media was receiving web-based abuse and negative comments or remarks from others, otherwise known as cyberbullying. This phenomenon had a long-lasting negative impact on students’ anxiety due to the intensely personal nature of the comments:

There were a few people who used to DM (direct message) me. They would talk about my appearance, the way I look and because I am quite self-conscious about that, they would use it to target me and harass me. Student 23

To the best of the authors’ knowledge, this study is one of the first qualitative studies exploring the perceptions of university students on the impact of social media on their anxiety. The results from the SLR suggest that social media use was associated with greater anxiety levels among university students and identified mediating pathways through which this association may occur: stress, envy, psychological capital, and a negative emotional state. Our first qualitative analysis added to the literature by identifying other pathways. These pathways were categorized into those that decrease or increase anxiety levels.

Principal Results

Theme 1: social connectivity.

The primary study identified that social connectivity plays a role in decreasing anxiety levels among university students. This route is consistent with the pathway of positive relationships proposed by [ 70 ] and in prior literature [ 71 ]. According to the 4 drives theory [ 72 ], humans have an intrinsic need to seek and develop mutual social commitments. Thus, our findings suggest that social media creates an alternative route through which students can connect and establish meaningful bonds with friends and family, satisfying such a need. Additionally, the relevance of social connectivity to decreased anxiety levels has been well established [ 73 , 74 ]. Contrary to such findings, Rae and Lonborg [ 41 ] found that those seeking new connections beyond friends and family experienced greater anxiety with increased social media use.

Our qualitative research revealed that students relied on social media for social connectivity during the COVID-19 pandemic. Dos Santos et al [ 75 ] highlighted the potential association between social isolation during the COVID-19 pandemic and symptoms of anxiety. Focusing on the unique circumstances of the university student cohort, who tend to be detached from their family and friends, the importance of social connectivity on anxiety may be greater, especially for international students [ 76 ]. Web-based community and support were another way through which students noted that social media reduced their anxiety. Humans display a need to belong, with prior literature highlighting that an increased sense of belonging (or subjective group identification) was associated with decreased anxiety [ 77 ].

Theme 2: Positive Experience

Positive content, such as spiritual, creative, informational, and activist content, was noted by students to reduce anxiety levels. For instance, activist content mentioned included campaigns such as the “Black Lives Matter” movement. This pathway could potentially be explained by the cohesion and sense of solidarity that activism provides, building a sense of unity and reducing anxiety levels [ 78 ]. Furthermore, engaging with spiritual content can lead to decreased anxiety levels, with individuals mentioning that some spiritual content was both grounding and hope-inducing, as supported by the literature [ 79 ].

Validation was highlighted as another positive experience that students had on social media. Assuming a student’s basic needs are secure, social media has the potential to fulfill self-esteem needs, encompassing the desire for reputation or prestige [ 80 ]. Student participants corroborated this positive effect of external validation through social media by noting the positive effect that it had on their present dispositions. This pathway for the alleviation of anxiety is explained by the fact that users have been found to receive endless joy so long as they receive positive validation in the form of likes and responsive comments [ 81 ]. Thus, when it comes to social media use, positive outcomes for students are inextricably linked to the positive responses that they receive [ 82 ].

Escapism has been defined as individuals choosing to get away from the reality that they live in due to unsatisfying life circumstances [ 83 ]. Students mentioned that sometimes social media provided them with an avenue to avoid negative emotional states such as anxiety by preoccupying themselves with distractions on social media. While escapism has generally been regarded in the literature as a negative and dysfunctional phenomenon [ 84 ], functional escapism has been found to be an effective coping mechanism to produce favorable well-being outcomes [ 85 ]. The description of social media use as a coping mechanism by students to alleviate anxiety follows the concept of self-suppression, as outlined by Stenseng et al [ 85 ]. While, in the short term, this provides students with a reprieve from their anxiety, it is only momentary and simply a tactic to minimize ill-being rather than one to maximize well-being [ 85 ]. That said, from our qualitative analyses, it was apparent that students viewed this as a virtue of social media, giving them an avenue to relax and find some relief in times when they may feel overly anxious.

The findings of this study support social comparison theory [ 37 ], which is the idea that one’s personal worth is established by self-evaluations determined by comparison to others. It was evident that students compared themselves to others on social media in 2 main ways: comparison of life and comparison of body types. Students described numerous occasions of upward comparison through social media. This is the act of comparing oneself to others perceived as superior in certain domains, such as social life, academia, wealth, and social status. Social comparison is well established as a pathway that increases the risk of anxiety [ 86 ]. Certain social media platforms, such as Instagram and Facebook, which are heavily image-based, have been identified as the most triggering comparisons [ 86 ]. Prior research has suggested that reduced self-esteem is another intermediate outcome resulting from social comparison, which in turn leads to greater anxiety [ 87 ]. This suggests that the relationship between social comparison and anxiety is complex and could involve other pathways.

Academic comparison on social media was found to be particularly prevalent within the university student cohort, which then led to anxiety. Competition is one possible explanation for this, as is fear of judgment by others on the internet [ 88 , 89 ]. Charlesworth [ 90 ] distinguishes 2 types of competition: integrative and aggressive. Students in this study displayed a mix of both, expressing happiness for their peers while also feeling hostile competitiveness owing to their own lack of achievement. Research has shown that anxiety can be triggered when one feels as if they are losing or failing [ 91 ]. Competition is not a driver for social media usage [ 92 ]; however, it can be hypothesized that individuals who are more competitive may be more prone to experiencing anxiety due to academic comparison on social media.

Comparison of one’s own body type to others seen on social media was especially common on image-based social media such as Instagram. This is corroborated by research that has revealed photo-based activity on social media to be linked to body image dissatisfaction. Kohler et al [ 37 ] found that beauty and fitness content marginally increased anxiety among university students. Our qualitative research demonstrated that while some students compared their bodies to images posted by their peers, others openly compared their bodies to influencers. Comparing one’s body to that of others briefly threatens one’s self-evaluation, causing feelings of anxiety [ 36 ].

FOMO, or the “fear of missing out,” was also identified as a pathway through which social media increased levels of anxiety symptoms. FOMO tends to arise through the perception of missing out, followed by actions to maintain these social connections [ 93 ]. Students mentioned that on social media platforms, individuals tend to share positive aspects of their lives, including group gatherings or partaking in various activities. Thus, propagating the feeling that their peers are engaging in more rewarding experiences. Drawing on the 4 drives theory [ 71 ], humans have a drive to acquire, thus fueling their need to match the actions and experiences of their peers. Our qualitative findings also identified that comparison drove the feeling of FOMO. This finding highlights the potential sequential nature of the mediating pathways, indicating that these pathways may not be linear in nature.

Procrastination was identified as another mediator, with increased social media use resulting in increased procrastination of tasks such as academic work or other daily activities, which in turn contributed to increased anxiety levels, corresponding with existing literature [ 94 ]. Additionally, qualitative findings outlined that procrastination on social media prevents students from completing other tasks such as exercise, a protective factor against anxiety [ 95 ].

Interestingly, the SLR exhibited mixed results for time spent on social media is associated with anxiety [ 30 , 31 ]. However, this may be due to students’ perceptions of their social media use; those who deem their time spent on social media as “waste” or procrastination may experience greater anxiety symptoms.

Consistent with prior literature [ 42 ], stress acted as a mediating pathway through which social media use drove anxiety levels among student participants. Our qualitative study highlights that this occurs as social media use provokes the overthinking of interactions, leaving students overwhelmed. This could be induced through excessive messages, explained by the theory of reciprocity, which results in individuals feeling obligated to reply to those who have taken the time to message them, otherwise resulting in reciprocity anxiety [ 96 ]. One of the key reasons mentioned that encouraged social media use was connectivity; however, it may be possible that a fine line exists between social media being beneficial for connections and acting as a stressor. Prior literature has identified that social media can drive stress levels, potentially through other mechanisms such as misinformation [ 97 ]. Thus, increased stress levels can drive anxiety, as explained by the increase in adverse psychological reactions caused by stress [ 98 , 99 ]. Students also highlighted that social media led them to overthink interactions, increasing their anxiety levels. However, overthinking interactions (or rumination) is a possible symptom of anxiety, and so overthinking interactions could be seen as an indication of heightened anxiety symptoms rather than a mediating pathway.

Our primary study defined negative experiences as encountering hostile web-based interactions in the form of cyberbullying, exposure to distressing and disturbing content, and the fear of judgment from others. Research suggests that cyberbullying and cybervictimization are prevalent in the student population [ 100 ], with students who are victims of cyberbullying being predicted to harbor higher levels of anxiety and even suicidal thoughts [ 100 - 102 ]. Conversely, some research has shown that anxiety predicts greater cyberbullying victimization among students, drawing a potential reverse pathway [ 103 ]. In our study, students described the fear of being judged by peers through social media. Many students spoke of the anxiety induced by posting content about themselves and the performance of that content. A qualitative study in the United Kingdom of people aged 8-20 years mirrored these concerns [ 104 ], acknowledging that the possible scrutiny from others prompted anxiety. This observation may be explained by the concept of self-presentation, defined by Leary [ 105 ] as “the process by which people convey to others that they are a certain kind of person.” Web-based self-presentation was shown to have a significant relationship with judgment anxiety [ 106 , 107 ]. However, among college students, strategic self-presentation is reported to be associated with increased well-being; thus, the link between self-presentation and anxiety still needs to be explored in depth [ 107 ].

Limitations and Future Research

This study has several limitations, which offer avenues for future research. First, this study relied on SSI for data collection; therefore, findings may be affected by mono-method bias [ 108 ]. The use of other quantitative and alternative qualitative methods may have resulted in a more rigorous analysis. Second, pilot interviews were conducted to test the effectiveness of our questions. All subsequent interviews were carried out by 2 groups of researchers, which helped create consistency in communication styles between interviews. However, this may result in interviewer bias; therefore, training and discussion throughout the interview process were used to minimize this effect. Third, despite reaching theoretical saturation and disseminating our recruitment material to different universities across the United Kingdom, our student sample mainly consisted of students from British Asian ethnic backgrounds (n=19) and those studying in London (n=26). Additionally, the majority of the participants were male (n=19). While the data was collected from a pool that was not directly reflective of the university population in terms of ethnicity proportions, gender ratio, and geographical location, the responses reached theoretical saturation, so further consequential interviews no longer added any new findings. However, we acknowledge that for future research within this domain, greater efforts will need to be made to ensure that participants adequately reflect the greater population. Due to the sensitivity of the subject of the interviews, both groups of participants were provided with a comprehensive informational sheet containing the details of the research. Thus, participants may be subjected to anchoring and availability bias during interviews. To mitigate this, participants were asked neutral, open questions, followed by specific questions on how social media benefits them. An effort was made to ensure that the positive impacts of social media on anxiety were explored in as much depth as the negative impacts.

To enhance confidence in the generalizability of the current findings, we encourage future research to widen the study sample to include participants from a greater number of universities across the United Kingdom, including a wider demographic that is more representative of the population of the United Kingdom. Furthermore, throughout our interviews with students, it was identified that certain social media platforms have a greater effect on SMIA. Participants mentioned that platforms, like Instagram, impacted them more than others, thus emphasizing the need for future research and drawing comparisons and granularity with respect to individual platforms. Moreover, anxiety was chosen due to its prevalence among university students. Nevertheless, other mental health symptoms, such as depressive symptoms, are also prevalent within populations and richly deserving of future study [ 109 ]. Hence, we encourage additional research that focuses on these other mental health symptoms that are also deserving of in-depth research.

This study sheds light on how university students perceive how social media affects their anxiety levels. The findings of this study can be used by many parties, including university counselors, students, and health care professionals, to create materials that will better prepare students to deal with anxious feelings that may arise from using social media.

Acknowledgments

The authors of this study would like to sincerely thank everyone who took part in the study for their time and valuable insights.

Abbreviations

Multimedia Appendix 1

Multimedia appendix 2, data availability.

Authors' Contributions: All 8 authors are joint first authors and have contributed equally to this study. ABE supervised this study. All authors approve of the final manuscript submitted.

Conflicts of Interest: None declared.

A literature review of the effects of social networking sites on secondary school students' academic achievement A literature review of the effects of social networking sites on secondary school students' academic achievement

• February 2021

• National Taiwan University of Science and Technology

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Lorraine Khavetsa Ingosi
• Silas Chepkwony
• Ronald Werunga Kikechi

• Wanting Zhang

• Ghilan Al-Madhagy Taufiq Hail
• Shafiz Affendi Mohd Yusof
• Ammar Rashid
• Abdalwali Lutfi

• LEARN INSTR

• Judy F. Chen

• Muhammad Irfan Aminudin
• Hery Sawiji
• Subroto Rapih
• Rachita Kansal
• Chander Diwaker

• Alamin Mydin

• Sex Res Soc Pol
• Cristian Molla-Esparza

• Int J Environ Res Publ Health

• Tatjana Gazibara
• Jelena Cakic
• Milica Cakic

• COMPUT HUM BEHAV

• COMPUT EDUC

• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up