Are Emotion-Expressing Messages More Shared on Social Media? A Meta-Analytic Review
Main Article Content
Abstract
Given that social media has brought significant change to the communication landscape, researchers have explored factors that can influence audiences’ information-sharing on social media such as a message feature like emotion-expressing. The present study meta-analytically summarized 19 studies to advance the understanding of the associations between emotion-expressing messages and information-sharing on social media in health and crisis communication contexts. Additional moderator analyses considered social media platform, sampling method, coding method, and emotion valence. Our study showed support for the social sharing of emotion hypothesis on social media; the findings showed that emotion-expressing messages are more likely to motivate audiences’ sharing behavior on social media in health and crisis contexts (r = .09, k = 19, N = 4,582,823). Moreover, we found that studies focusing on non-Twitter platforms (vs. Twitter), using nonrandom sampling (vs. using random sampling or all samples), using human coding (vs. machine coding), and focusing on messages expressing positive emotions (vs. negative emotions or both positive and negative emotions) had larger effect sizes. The study suggested implications for the future development of a theoretical framework on emotion-expressing messages and information-sharing. It also informed communication practices of broadening the reach of health and crisis information.
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References
Abelson, R. P. (1985). A variance explanation paradox: When a little is a lot. Psychological Bulletin, 97(1), 129–133. https://doi.org/10.1037/0033-2909.97.1.129
*Ali, K., Zain-ul-abdin, K., Li, C., Ali, A. A., & Carcioppolo, N. (2019). Viruses going viral: Impact of fear-arousing sensationalist social media messages on user engagement. Science Communication, 41(3), 314–338. https://doi.org/10.1177/1075547019846124
Bazarova, N. N. (2012). Public intimacy: Disclosure interpretation and social judgments on Facebook. Journal of Communication, 62(5), 815–832. https://doi.org/10.1111/j.1460-2466.2012.01664.x
Bartoš, F., & Schimmack, U. (2020). Z-curve. 2.0: Estimating replication rates and discovery rates. PsyArXiv. http://doi.org/10.31234/osf.io/urgtn
Cheung, M. W. L. (2019). A guide to conducting a meta-analysis with non-independent effect sizes. Neuropsychology Review, 29, 387–396. https://doi.org/10.1007/s11065-019-09415-6
Cohen, J. (1969). Statistical power analysis for the behavioral sciences. Academic Press.
Curci, A., & Bellelli, G. (2004). Cognitive and social consequences of exposure to emotional narratives: Two studies on secondary social sharing of emotions. Cognition and Emotion, 18(7), 881–900. https://doi.org/10.1080/02699930341000347
Cmeciu, C., & Coman, I. (2018). Twitter as a means of emotional coping and collective (re)framing of crises. Case study: The “colectiv” crisis in Romania. Social Communication, 4(2), 6–15. https://doi.org/10.2478/sc-2018-0011
Cohen, E. L., & Hoffner, C. (2016). Finding meaning in a celebrity’s death: The relationship between parasocial attachment, grief, and sharing educational health information related to Robin Williams on social network sites. Computers in Human Behavior, 65, 643–650. https://doi.org/10.1016/j.chb.2016.06.042
Chervonsky, E., & Hunt, C. (2017). Suppression and expression of emotion in social and interpersonal outcomes: A meta-analysis. Emotion, 17(4), 669–683. http://doi.org/10.1037/emo0000270
Christophe, V., & Rimé, B. (1997). Exposure to the social sharing of emotion: Emotional impact, listener responses and secondary social sharing. European Journal of Social Psychology, 27(1), 37–54. https://doi.org/10.1002/(SICI)1099-0992(199701)27:1<37::AID-EJSP806>3.0.CO;2-1
*Chen, R., & Sakamoto, Y. (2013). Perspective matters: Sharing of crisis information in social media. In Proceedings of the 46th Annual Hawaii International Conference on System Sciences, HI, USA, 2033–2041. http://doi.org/10.1109/HICSS.2013.447
Choi, M., & Toma, C. L. (2014). Social sharing through interpersonal media: Patterns and effects on emotional well-being. Computers in Human Behavior, 36, 530–541. https://doi.org/10.1016/j.chb.2014.04.026
Choi, M., & Toma, C. L. (2021). Understanding mechanisms of media use for the social sharing of emotion: The role of media affordances and habitual media use. Journal of Media Psychology: Theories, Methods, and Applications. https://doi.org/10.1027/1864-1105/a000301
Conn, V. S., Valentine, J. C., Cooper, H. M., & Rantz, M. J. (2003). Grey literature in meta-analyses. Nursing Research, 52(4), 256–261. https://doi.org/10.1097/00006199-200307000-00008
Dunlop, S., Wakefield, M., & Kashima, Y. (2008). Can you feel it? Negative emotion, risk, and narrative in health communication. Media Psychology, 11(1), 52–75. https://doi.org/10.1080/15213260701853112
Freelon, D. G. (2010). ReCal: Intercoder reliability calculation as a web service. International Journal of Internet Science, 5(1), 20–33.
Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168. https://doi.org/10.1177/2515245919847202
Gilpin, A. R. (1993). Table for conversion of Kendall’s Tau to Spearman’s Rho within the context of measures of magnitude of effect for meta-analysis. Educational and Psychological Measurement, 53(1), 87–92. https://doi.org/10.1177/0013164493053001007
*Gurman, T. A., & Clark, T. (2016). Findings and implications from a quantitative content analysis of tweets about emergency contraception. Digital Health, 2, 2055–2076. https://doi.org/10.1177/2055207615625035
Hall, M., & Caton, S. (2017). Am I who I say I am? Unobtrusive self-representation and personality recognition on Facebook. PloS One, 12(9), e0184417. https://doi.org/10.1371/journal.pone.0184417
*Hyvärinen, H., & Beck, R. (2019). Fear and loathing in Boston: the roles of different emotions in information sharing on social media following a terror attack. In Proceedings of the 27th European Conference on Information Systems (ECIS).
Hunter, J., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings (2nd ed.). Sage.
*Harvey, A. M., Thompson, S., Lac, A., & Coolidge, F. L. (2019). Fear and derision: A quantitative content analysis of provaccine and antivaccine Internet memes. Health Education & Behavior, 46(6), 1012–1023. https://doi.org/10.1177/1090198119866886
Jawad, M., Abass, J., Hariri, A., & Akl, E. A. (2015). Social media use for public health campaigning in a low resource setting: The case of waterpipe tobacco smoking. BioMed Research International, 2015, 1–4. https://doi.org/10.1155/2015/562586
Jin, Y., Fraustino, J. D., & Liu, B. F. (2016). The scared, the outraged, and the anxious: How crisis emotions, involvement, and demographics predict publics’ conative coping. International Journal of Strategic Communication, 10(4), 289–308. https://doi-org/10.1080/1553118X.2016.1160401
Krippendorff, K. (2004). Reliability in content analysis: Some common misconceptions and recommendations. Human Communication Research, 30(3), 411–433. https://doi.org/10.1111/j.1468-2958.2004.tb00738.x
*Kalandar, A., Al-Youha, S., Al-Halabi, B., & Williams, J. (2018). What does the public think? Examining plastic surgery perceptions through the Twitterverse. Plastic and Reconstructive Surgery, 142(1), 265–274. https://doi.org/10.1097/PRS.0000000000004484
Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. In Proceedings of the National Academy of Sciences, 111(24), 8788–8790. https://doi.org/10.1073/pnas.1320040111
*Kim, H. S. (2015). Attracting views and going viral: How message features and news-sharing channels affect health news diffusion. Journal of Communication, 65(3), 512–534. https://doi.org/10.1111/jcom.12160
*Kiriya, J., Edwards, P., & Roberts, I. (2018). Effect of emotional content on online video sharing among health care professionals and researchers (DIFFUSION): Results and lessons learnt from a randomised controlled trial. BMJ Open, 8(4), e019419. http://dx.doi.org/10.1136/bmjopen-2017-019419
*Kim, E., Hou, J., Han, J. Y., & Himelboim, I. (2016). Predicting retweeting behavior on breast cancer social networks: Network and content characteristics. Journal of Health Communication, 21(4), 479–486. https://doi.org/10.1080/10810730.2015.1103326
Langston, C. A. (1994). Capitalizing on and coping with daily-life events: Expressive responses to positive events. Journal of Personality and Social Psychology, 67(6), 1112–1125. https://doi.org/10.1037/0022-3514.67.6.1112
Lu, X., & Jin, Y. (2020). Information vetting as a key component in social-mediated crisis communication: An exploratory study to examine the initial conceptualization. Public Relations Review, 46(2), 101891. https://doi.org/10.1016/j.pubrev.2020.101891
Lenhard, W., & Lenhard, A. (2016). Calculation of effect sizes. Psychometrica https://www.psychometrica.de/effect_size.html
Lin, K. Y., & Lu, H. P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152–1161. https://doi.org/10.1016/j.chb.2010.12.009
Liang, Y., & Kee, K. F. (2018). Developing and validating the ABC framework of information diffusion on social media. New Media & Society, 20(1), 272–292. https://doi.org/10.1177/1461444816661552
Lieberman, A., & Schroeder, J. (2020). Two social lives: How differences between online and offline interaction influence social outcomes. Current Opinion in Psychology, 31, 16–21. https://doi.org/10.1016/j.copsyc.2019.06.022
*Lin, W. Y., Zhang, X., & Cao, B. (2018). How do new media influence youths’ health literacy? Exploring the effects of media channel and content on safer sex literacy. International Journal of Sexual Health, 30(4), 354–365. https://doi.org/10.1080/19317611.2018.1509921
*Lohmann, S., White, B. X., & Zhen, Z. U. O. (2018). HIV messaging on Twitter: An analysis of current practice and data-driven recommendations. AIDS, 32(18), 2799–2805. https://doi.org/10.1097/QAD.0000000000002018
Li, L., Zhang, Q., Wang, X., Zhang, J., Wang, T., Gao, T. L., ... & Wang, F. Y. (2020). Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on Computational Social Systems, 7(2), 556–562. http://doi.org/10.1109/TCSS.2020.2980007
Mehta, A. M., Liu, B. F., Tyquin, E., & Tam, L. (2021). A process view of crisis misinformation: How public relations professionals detect, manage, and evaluate crisis misinformation. Public Relations Review, 47(2), 102040. https://doi.org/10.1016/j.pubrev.2021.102040
*Mou, Y., & Shen, F. (2018). (Potential) parents like me: Testing the effects of user-generated health content on social media. Chinese Journal of Communication, 11(2), 186–201. https://doi.org/10.1080/17544750.2017.1386221
Maitlis, S., & Sonenshein, S. (2010). Sensemaking in crisis and change: Inspiration and insights from Weick (1988). Journal of Management Studies, 47(3), 551–580. https://doi.org/10.1111/j.1467-6486.2010.00908.x
*Park, M. (2019). Information sharing to promote risky health behavior on social media. Journal of Health Communication, 24(4), 359–367. https://doi.org/10.1080/10810730.2019.1604914
Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90(1), 175–181. https://doi.org/10.1037/0021-9010.90.1.175
Qiu, L., Lin, H., Leung, A. K., & Tov, W. (2012). Putting their best foot forward: Emotional disclosure on Facebook. Cyberpsychology, Behavior, and Social Networking, 15(10), 569–572. https://doi.org/10.1089/cyber.2012.0200
Rosenthal, R., & DiMatteo, M. R. (2001). Meta-analysis: Recent developments in quantitative methods for literature review. Annual Review of Psychology, 52, 59–82. http://doi.org/10.1146/annurev.psych.52.1.59
Rimé, B., Mesquita, B., Boca, S., & Philippot, P. (1991). Beyond the emotional event: Six studies on the social sharing of emotion. Cognition & Emotion, 5(5–6), 435–465. https://doi.org/10.1080/02699939108411052
Rimé, B. (2009). Emotion elicits the social sharing of emotion: Theory and empirical review. Emotion Review, 1(1), 60–85. https://doi.org/10.1177/1754073908097189
Rimé, B., Finkenauer, C., Luminet, O., Zech, E., & Philippot, P. (1998). Social sharing of emotion: New evidence and new questions. European Review of Social Psychology, 9(1), 145–189. https://doi.org/10.1080/14792779843000072
Rimé, B., Kanyangara, P., Yzerbyt, V., & Paez, D. (2011). The impact of Gacaca tribunals in Rwanda: Psychosocial effects of participation in a truth and reconciliation process after a genocide. European Journal of Social Psychology, 41(6), 695–706.
Schachter, S. (1959). The psychology of affiliation. Stanford University Press.
Subramanian, K. R. (2017). Influence of social media in interpersonal communication. International Journal of Scientific Progress and Research, 38(2), 70–75.
Sedgwick, P. (2015). What is publication bias in a meta-analysis?. BMJ, 351(8022), h4419. https://doi.org/10.1136/bmj.h4419
*Sumner, S. A., Bowen, D. A., & Bartholow, B. (2020). Factors associated with increased dissemination of positive mental health messaging on social media. Crisis: The Journal of Crisis Intervention and Suicide Prevention, 41(2), 141–145. https://doi.org/10.1027/0227-5910/a000598
Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248. https://doi.org/10.2753/MIS0742-1222290408
Sellnow, D. D., Lane, D. R., Sellnow, T. L., & Littlefield, R. S. (2017). The IDEA model as a best practice for effective instructional risk and crisis communication. Communication Studies, 68(5), 552–567. https://doi.org/10.1080/10510974.2017.1375535
Stellefson, M., Paige, S., Apperson, A., & Spratt, S. (2019). Social media content analysis of public diabetes Facebook groups. Journal of Diabetes Science and Technology, 13(3), 428–438. https://doi.org/10.1177/1932296819839099
Shrout, P. E., & Rodgers, J. L. (2018). Psychology, science, and knowledge construction: Broadening perspectives from the replication crisis. Annual Review of Psychology, 69, 487–510. https://doi.org/10.1146/annurev-psych-122216-011845
Scammacca, N., Roberts, G., & Stuebing, K. K. (2014). Meta-analysis with complex research designs: Dealing with dependence from multiple measures and multiple group comparisons. Review of Educational Research, 84(3), 328–364. http://doi.org/10.3102/0034654313500826
Sterne, J. A., Sutton, A. J., Ioannidis, J. P., Terrin, N., Jones, D. R., Lau, J., ... & Tetzlaff, J. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ, 343, d4002. https://doi.org/10.1136/bmj.d4002
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
Vaterlaus, J. M., Patten, E. V., Roche, C., & Young, J. A. (2015). #Gettinghealthy: The perceived influence of social media on young adult health behaviors. Computers in Human Behavior, 45, 151–157. https://doi.org/10.1016/j.cnb.2014.12.013
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. http://doi.org/10.1126/science.aap9559
Vermeulen, A., Vandebosch, H., & Heirman, W. (2018). # Smiling,# venting, or both? Adolescents’ social sharing of emotions on social media. Computers in Human Behavior, 84, 211–219. https://doi.org/10.1016/j.chb.2018.02.022
*Wang, X., Chen, L., Shi, J., & Peng, T. (2019). What makes cancer information viral on social media? Computers in Human Behavior, 93, 149–156. https://doi.org/10.1016/j.chb.2018.12.024
Wang, J., & Wei, L. (2020). Fear and hope, bitter and sweet: Emotion sharing of cancer community on twitter. Social Media+ Society, 6(1), 2056305119897319. https://doi.org/10.1177/2056305119897319
*Xu, W. W., & Zhang, C. (2018). Sentiment, richness, authority, and relevance model of information sharing during social crises—the case of# MH370 tweets. Computers in Human Behavior, 89, 199–206. https://doi.org/10.1016/j.chb.2018.07.041
Yoo, W., Paek, H. J., & Hove, T. (2020). Differential effects of content-oriented versus user-oriented social media on risk perceptions and behavioral intentions. Health Communication, 35(1), 99–109. https://doi-org/10.1080/10410236.2018.1545169
*Zhu, X., Kim, Y., & Park, H. (2020). Do messages spread widely also diffuse fast? Examining the effects of message characteristics on information diffusion. Computers in Human Behavior, 103, 37–47. https://doi.org/10.1016/j.chb.2019.09.006
*Zhou, J., Liu, F., & Zhou, H. (2018). Understanding health food messages on Twitter for health literacy promotion. Perspectives in Public Health, 138(3), 173–179. https://doi.org/10.1177/1757913918760359
*Zhang, L., Xu, L., & Zhang, W. (2017). Social media as amplification station: Factors that influence the speed of online public response to health emergencies. Asian Journal of Communication, 27(3), 322–338. https://doi.org/10.1080/01292986.2017.1290124