Social media (SM) may confer emotional benefits via connection with others. However, epidemiologic studies suggest that overall SM is paradoxically associated with increased depressive symptoms. To better understand these findings, we examined the association between positive and negative experiences on SM and depressive symptoms.

Methods

We conducted a cross-sectional survey of 1,179 full-time students at the University of West Virginia, aged 18 to 30, in August 2016. Independent variables were self-reported positive and negative experiences on SM. The dependent variable was depressive symptoms as measured using the Patient-Reported Outcomes Measurement Information System. We used multivariable logistic regression to assess associations between SM experiences and depressive symptoms controlling for sociodemographic factors including age, sex, race/ethnicity, education, relationship status, and living situation.

Results

Of the 1,179 participants, 62% were female, 28% were non-White, and 51% were single. After controlling for covariates, each 10% increase in positive experiences on SM was associated with a 4% decrease in odds of depressive symptoms, but this was not statistically significant (adjusted odds ratio [AOR] = 0.96; 95% confidence interval [CI] = 0.91–1.002). However, each 10% increase in negative experiences was associated with a 20% increase in odds of depressive symptoms (AOR = 1.20; 95% CI = 1.11–1.31). When both independent variables were included in the same model, the association between negative experiences and depressive symptoms remained significant (AOR = 1.19, 95% CI = 1.10–1.30).

Conclusions

Negative experiences online may have higher potency than positive ones because of negativity bias. Future research should examine temporality to determine if it is also possible that individuals with depressive symptomatology are inclined toward negative interactions.

CONFLICT OF INTEREST

The authors have no conflict of interest to report. The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements, of the United States and received approval from the Institutional Review Board of the University of West Virginia.

REFERENCES

Acar, A. (2008). Antecedents and consequences of online social networking behavior: The case of Facebook. Journal of Website Promotion, 3(1-2), 62–83. https://doi.org/10.1080/15533610802052654
10.1080/15533610802052654
Google Scholar
American Psychiatric Association. (2013). LEVEL 2—Depression—Adult (PROMIS emotional distress—depression—Short form). Retrieved from https://www.webcitation.org/6eVeBFb6E
Google Scholar
Becker, M. W., Alzahabi, R., & Hopwood, C. J. (2013). Media multitasking is associated with symptoms of depression and social anxiety. Cyberpsychology, Behavior, and Social Networking, 16(2), 132–135. https://doi.org/10.1089/cyber.2012.0291
10.1089/cyber.2012.0291
PubMed Web of Science® Google Scholar
Bickham, D. S., Hswen, Y., & Rich, M. (2015). Media use and depression: Exposure, household rules, and symptoms among young adolescents in the USA. International Journal of Public Health, 60(2), 147–155. https://doi.org/10.1007/s00038-014-0647-6
10.1007/s00038-014-0647-6
PubMed Web of Science® Google Scholar
Cain, M. S., Leonard, J. A., Gabrieli, J. D. E., & Finn, A. S. (2016). Media multitasking in adolescence. Psychonomic Bulletin & Review, 23, 1932–1941. https://doi.org/10.3758/s13423-016-1036-3
10.3758/s13423-016-1036-3
PubMed Web of Science® Google Scholar
Caplan, S. E. (2003). Preference for online social interaction: A theory of problematic Internet use and psychosocial well-being. Communication Research, 30(6), 625–648. https://doi.org/10.1177/0093650203257842
10.1177/0093650203257842
Web of Science® Google Scholar
Cassidy, W., Faucher, C., & Jackson, M. (2017). Adversity in university: Cyberbullying and its impacts on students, faculty and administrators. International Journal of Environmental Research and Public Health, 14(8). https://doi.org/10.3390/ijerph14080888
10.3390/ijerph14080888
PubMed Web of Science® Google Scholar
Cella, D., Gershon, R., Bass, M., & Rothrock, N. (2015). Assessment center user manual: PROMIS depression scoring manual. Retrieved from https://www.webcitation.org/6sDme4hlP
Google Scholar
Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., … Hays, R. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194. https://doi.org/10.1016/j.jclinepi.2010.04.011
10.1016/j.jclinepi.2010.04.011
PubMed Web of Science® Google Scholar
Choi, S. W., Schalet, B., Cook, K. F., & Cella, D. (2014). Establishing a common metric for depressive symptoms: Linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychological Assessment, 26(2), 513–527. https://doi.org/10.1037/a0035768
10.1037/a0035768
PubMed Web of Science® Google Scholar
Chou, H. T. G., & Edge, N. (2012). “They are happier and having better lives than I am”: The impact of using Facebook on perceptions of others’ lives. Cyberpsychology, Behavior and Social Networking, 15(2), 117–121. https://doi.org/10.1089/cyber.2011.0324
10.1089/cyber.2011.0324
PubMed Web of Science® Google Scholar
Cover, R. (2015). Digital identities: Creating and communicating the online self. London, England: Elsevier.
Google Scholar
Duggan, M., & Brenner, J. (2013). The demographics of social media users—2012. Washington, DC: Pew Research Center. Retrieved from https://pewinternet.org/Reports/2013/Social-media-users.aspx
Google Scholar
Ellison, N. B., Heino, R., & Gibbs, J. (2006). Managing impressions online: Self-presentation processes in the online dating environment. Journal of Computer-Mediated Communication, 11, 415–441. https://doi.org/10.1111/j.1083-6101.2006.00020.x
10.1111/j.1083-6101.2006.00020.x
Web of Science® Google Scholar
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends”: Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168. https://doi.org/10.1111/j.1083-6101.2007.00367.x
10.1111/j.1083-6101.2007.00367.x
Web of Science® Google Scholar
Ellison, N. B., Steinfield, C., & Lampe, C. (2011). Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media & Society, 13(6), 873–892. https://doi.org/10.1177/1461444810385389
10.1177/1461444810385389
Web of Science® Google Scholar
Ellison, N. B., Vitak, J., Gray, R., & Lampe, C. (2014). Cultivating social resources on social network sites: Facebook relationship maintenance behaviors and their role in social capital processes. Journal of Computer-Mediated Communication, 19(4), 855–870. https://doi.org/10.1111/jcc4.12078
10.1111/jcc4.12078
Web of Science® Google Scholar
Gottfried, J., & Shearer, E. (2016). News use across social media platforms 2016. Washington, DC: Pew Research Center.
Google Scholar
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086/225469
10.1086/225469
Web of Science® Google Scholar
Greenberg, P. E., Fournier, A.-A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The economic burden of adults with major depressive disorder in the United States (2005 and 2010). Journal of Clinical Psychiatry, 76(2), 155–162. https://doi.org/10.4088/JCP.14m09298
10.4088/JCP.14m09298
PubMed Web of Science® Google Scholar
Greenwood, S., Perrin, A., & Duggan, M. (2016). Social media update 2016. Washington, DC: Pew Research Center. Retrieved from https://www.webcitation.org/6r4NbntGd
Google Scholar
Grubb, H. M. (2016). Marginalization of transgender identities: Implications for health equity. Psychiatric Annals, 46(6), 334–339. https://doi.org/10.3928/00485713-20160418-01
10.3928/00485713-20160418-01
Web of Science® Google Scholar
Hill, R. A., & Dunbar, R. I. M. (2003). Social network size in humans. Human Nature, 14(1), 53–72. https://doi.org/10.1007/s12110-003-1016-y
10.1007/s12110-003-1016-y
CAS PubMed Web of Science® Google Scholar
Kim, J., & Lee, J. R. (2011). The Facebook paths to happiness: Effects of the number of Facebook friends and self-presentation on subjective well-being. Cyberpsychology, Behavior and Social Networking, 14(6), 359–364. https://doi.org/10.1089/cyber.2010.0374
10.1089/cyber.2010.0374
PubMed Web of Science® Google Scholar
Krackhardt, D. (1992). The strength of strong ties: The importance of Philos in organizations. In N. Nohria & R. Eccles (Eds.), Networks and organizations: Structure, form, and action. Cambridge, MA: Harvard Business School Press.
Web of Science® Google Scholar
Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., … Ybarra, O. (2013). Facebook use predicts declines in subjective well-being in young adults. PLOS One, 8(8), e69841. https://doi.org/10.1371/journal.pone.0069841
10.1371/journal.pone.0069841
CAS PubMed Web of Science® Google Scholar
Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., … Primack, B. A. (2016). Association between social media use and depression among U.S. young adults. Depression and Anxiety, 33(4), 323–331. https://doi.org/10.1002/da.22466
10.1002/da.22466
PubMed Web of Science® Google Scholar
Lou, L. L., Yan, Z., Nickerson, A., & McMorris, R. (2012). An examination of the reciprocal relationship of loneliness and Facebook use among first-year college students. Journal of Educational Computing Research, 46(1), 105–117. https://doi.org/10.2190/EC.46.1.e
10.2190/EC.46.1.e
Web of Science® Google Scholar
Lup, K., Trub, L., & Rosenthal, L. (2015). Instagram #instasad?: Exploring associations among Instagram use, depressive symptoms, negative social comparison, and strangers followed. Cyberpsychology, Behavior, and Social Networking, 18(5), 247–252. https://doi.org/10.1089/cyber.2014.0560
10.1089/cyber.2014.0560
PubMed Web of Science® Google Scholar
Manago, A. M., Taylor, T., & Greenfield, P. M. (2012). Me and my 400 friends: The anatomy of college students’ Facebook networks, their communication patterns, and well-being. Developmental Psychology, 48(2), 369–380. https://doi.org/10.1037/a0026338
10.1037/a0026338
PubMed Web of Science® Google Scholar
McDougall, M. A., Walsh, M., Wattier, K., Knigge, R., Miller, L., Stevermer, M., & Fogas, B. S. (2016). The effect of social networking sites on the relationship between perceived social support and depression. Psychiatry Research, 246, 223–229. https://doi.org/10.1016/j.psychres.2016.09.018
10.1016/j.psychres.2016.09.018
PubMed Web of Science® Google Scholar
National Institute of Mental Health. (2016). Use of mental health services and treatment among children. Retrieved from https://www.webcitation.org/6gZH1xtay
Google Scholar
O'Keeffe, G. S., & Clarke-Pearson, K. (2011). The impact of social media on children, adolescents, and families. American Academy of Pediatrics, 127(4), 800–804. https://doi.org/10.1542/peds.2011-0054
Google Scholar
Pantic, I., Damjanovic, A., Todorovic, J., Topalovic, D., Bojovic-Jovic, D., Ristic, S., & Pantic, S. (2012). Association between online social networking and depression in high school students: Behavioral physiology viewpoint. Psychiatria Danubina, 24, 90–93.
PubMed Web of Science® Google Scholar
Paxon, P. (1999). Is social capital declining in the United States? A multiple indicator assessment. American Journal of Sociology, 105(1), 88–127. https://doi.org/10.1086/210268
10.1086/210268
Web of Science® Google Scholar
Perrin, A. (2015). Social media usage: 2005–2015. Retrieved from https://www.pewinternet.org/2015/10/08/social-networking-usage-2005-2015/#
Google Scholar
Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., & Cella, D. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263–283. https://doi.org/10.1177/1073191111411667
10.1177/1073191111411667
PubMed Web of Science® Google Scholar
Pilkonis, P. A., Yu, L., Dodds, N. E., Johnston, K. L., Maihoefer, C. C., & Lawrence, S. M. (2014). Validation of the depression item bank from the Patient-Reported Outcomes Measurement Information System (PROMIS) in a three-month observational study. Journal of Psychiatric Research, 56, 112–119. https://doi.org/10.1016/j.jpsychires.2014.05.010
10.1016/j.jpsychires.2014.05.010
PubMed Web of Science® Google Scholar
Pratt, L. A., & Brody, D. J. (2014). Depression in U.S. household population, 2009–2012. Hyattsville, MD: U.S. Department of Health & Human Services. Retrieved from https://www.cdc.gov/nchs/data/databriefs/db81.pdf
Google Scholar
Primack, B. A., Swanier, B., Georgiopoulos, A. M., Land, S. R., Fine, M. J., Swainer, B., … Fine, M. J. (2009). Association between media use in adolescence and depression in young adulthood: A longitudinal study. Archives of General Psychiatry, 66(2), 181–188. https://doi.org/10.1001/archgenpsychiatry.2008.532
10.1001/archgenpsychiatry.2008.532
PubMed Web of Science® Google Scholar
Qualtrics. (2015). Qualtrics. Retrieved from https://www.qualtrics.com
Google Scholar
Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4), 296–320. https://doi.org/10.1207/S15327957PSPR0504
10.1207/S15327957PSPR0504_2
Web of Science® Google Scholar
SAMHSA. (2015). Behavioral health trends in the United States: Results from the 2014 national survey on drug use and health. Retrieved from https://www.samhsa.gov/data/
Google Scholar
Shensa, A., Sidani, J. E., Lin, L., Bowman, N., & Primack, B. A. (2016). Social media use and perceived emotional support among US young adults. Journal of Community Health, 41(3), 541–549. https://doi.org/10.1007/s10900-015-0128-8
10.1007/s10900-015-0128-8
PubMed Web of Science® Google Scholar
StataCorp. (2016). Stata Statistical Software (Version 14). College Station, TX: StataCorp.
Google Scholar
Steinfield, C., Ellison, N. B., & Lampe, C. (2008). Social capital, self-esteem, and use of online social network sites: A longitudinal analysis. Journal of Applied Developmental Psychology, 29(6), 434–445. https://doi.org/10.1016/j.appdev.2008.07.002
10.1016/j.appdev.2008.07.002
Web of Science® Google Scholar
Twenge, J. M. (2015). Time period and birth cohort differences in depressive symptoms in the U.S., 1982–2013. Social Indicators Research, 121(2), 437–454. https://doi.org/10.1007/s11205-014-0647-1
10.1007/s11205-014-0647-1
Web of Science® Google Scholar
Whiting, A., & Williams, D. (2013). Why people use social media: A uses and gratifications approach. Qualitative Market Research: An International Journal, 16(4), 362–369. https://doi.org/10.1108/QMR-06-2013-0041
10.1108/QMR-06-2013-0041
Google Scholar
World Health Organization. (2016). Depression fact sheet. Retrieved from https://www.who.int/mediacentre/factsheets/fs369/en/
Google Scholar
Xu, S., Wang, Z., & David, P. (2016). Media multitasking and well-being of university students. Computers in Human Behavior, 55(Part A), 242–250. https://doi.org/10.1016/j.chb.2015.08.040
10.1016/j.chb.2015.08.040
Web of Science® Google Scholar

Citing Literature

All articles

The association between valence of social media experiences and depressive symptoms

Abstract

Background

Methods

Results

Conclusions

CONFLICT OF INTEREST

REFERENCES

Citing Literature

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

The association between valence of social media experiences and depressive symptoms

Abstract

Background

Methods

Results

Conclusions

CONFLICT OF INTEREST

REFERENCES

Citing Literature

References

Related

Information