Skip to main content
Short Report

Factors Associated With Increased Dissemination of Positive Mental Health Messaging On Social Media

Published Online:https://doi.org/10.1027/0227-5910/a000598

Abstract.Background: The dissemination of positive messages about mental health is a key goal of organizations and individuals. Aims: Our aim was to examine factors that predict increased dissemination of such messages. Method: We analyzed 10,998 positive messages authored on Twitter and studied factors associated with messages that are shared (re-tweeted) using logistic regression. Characteristics of the account, message, linguistic style, sentiment, and topic were examined. Results: Less than one third of positive messages (31.7%) were shared at least once. In adjusted models, accounts that posted a greater number of messages were less likely to have any single message shared. Messages about military-related topics were 60% more likely to be shared (adjusted odds ratio [AOR] = 1.6, 95% CI [1.1, 2.1]) as well as messages containing achievement-related keywords (AOR = 1.6, 95% CI [1.3, 1.9]). Conversely, positive messages explicitly addressing eating/food, appearance, and sad affective states were less likely to be shared. Multiple other message characteristics influenced sharing. Limitations: Only messages on a single platform and over a focused period of time were analyzed. Conclusion: A knowledge of factors affecting dissemination of positive mental health messages may aid organizations and individuals seeking to promote such messages online.

References

  • Burnap, P., Colombo, W., & Scourfield, J. (2015, September). Machine classification and analysis of suicide-related communication on twitter. Paper presented at the 26th ACM Conference on Hypertext & Social Media in Guzelyurt, Northern Cyprus. Retrieved from 10.1145/2700171.2791023 First citation in articleCrossrefGoogle Scholar

  • Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. (2017). Web-based Injury Statistics Query and Reporting System. Retrieved from http://www.cdc.gov/injury/wisqars/index.html First citation in articleGoogle Scholar

  • Colombo, G. B., Burnap, P., Hodorog, A., & Scourfield, J. (2016). Analysing the connectivity and communication of suicidal users on twitter. Computer Communications, 73, 291–300. 10.1016/j.comcom.2015.07.018. First citation in articleCrossref MedlineGoogle Scholar

  • De Choudhury, M., Counts, S., & Horvitz, E. (2013). Social media as a measurement tool of depression in populations. Proceedings of the 5th Annual ACM Web Science Conference, 47–56. Retrieved from 10.1145/2464464.2464480 First citation in articleCrossrefGoogle Scholar

  • De Choudhury, M., & Kıcıman, E. (2017, May). The language of social support in social media and its effect on suicidal ideation risk. Paper presented at the International AAAI Conference on Weblogs and Social Media in Montreal, Canada. First citation in articleGoogle Scholar

  • Dunlop, S. M., More, E., & Romer, D. (2011). Where do youth learn about suicides on the Internet, and what influence does this have on suicidal ideation? Journal of Child Psychology and Psychiatry, 52(10), 1073–1080. 10.1111/j.1469-7610.2011.02416.x First citation in articleCrossref MedlineGoogle Scholar

  • Jashinsky, J., Burton, S. H., Hanson, C. L., West, J., Giraud-Carrier, C., Barnes, M. D., & Argyle, T. (2014). Tracking suicide risk factors through Twitter in the US. Crisis. 10.1027/0227-5910/a000234 First citation in articleLinkGoogle Scholar

  • 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. 10.1080/10810730.2015.1103326 First citation in articleCrossref MedlineGoogle Scholar

  • Lester, D. (1987). Suicide as a learned behavior. Springfield, IL: Charles C. Thomas. First citation in articleGoogle Scholar

  • Mitchell, B. G., Russo, P. L., Otter, J. A., Kiernan, M. A., & Aveling, L. (2017). What makes a tweet fly? Analysis of Twitter messaging at four infection control conferences. Infection Control & Hospital Epidemiology, 38(11), 1271–1276. 10.1017/ice.2017.170 First citation in articleCrossref MedlineGoogle Scholar

  • O'Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., & Christensen, H. (2015). Detecting suicidality on Twitter. Internet Interventions, 2(2), 183–188. 10.1016/j.invent.2015.03.005 First citation in articleCrossrefGoogle Scholar

  • Robinson, J., Cox, G., Bailey, E., Hetrick, S., Rodrigues, M., Fisher, S., & Herrman, H. (2016). Social media and suicide prevention: A systematic review. Early Intervention in Psychiatry, 10(2), 103–121. 10.1111/eip.12229 First citation in articleCrossref MedlineGoogle Scholar

  • Shah, A. (2010). The relationship between general population suicide rates and the Internet: A cross-national study. Suicide and Life-Threatening Behavior, 40(2), 146–150. 10.1521/suli.2010.40.2.146 First citation in articleCrossref MedlineGoogle Scholar

  • So, J., Prestin, A., Lee, L., Wang, Y., Yen, J., & Chou, W.-Y. S. (2016). What do people like to "share" about obesity? A content analysis of frequent retweets about obesity on Twitter. Health Communication, 31(2), 193–206. 10.1080/10410236.2014.940675 First citation in articleCrossref MedlineGoogle Scholar

  • Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 3–17. 10.1177%2F2167702617723376 First citation in articleCrossrefGoogle Scholar