#MyDepressionLooksLike: Examining Public Discourse About Depression on Twitter

Overview

Journal JMIR Ment Health

Specialties Health Services
Psychiatry
Psychology

Date 2017 Oct 20

PMID 29046270

Citations 35

Authors

E Megan Lachmar

Andrea K Wittenborn

Katherine W Bogen

Heather L McCauley

Affiliations

Soon will be listed here.

Abstract

Background: Social media provides a context for billions of users to connect, express sentiments, and provide in-the-moment status updates. Because Twitter users tend to tweet emotional updates from daily life, the platform provides unique insights into experiences of mental health problems. Depression is not only one of the most prevalent health conditions but also carries a social stigma. Yet, opening up about one's depression and seeking social support may provide relief from symptoms.

Objective: The aim of this study was to examine the public discourse of the trending hashtag #MyDepressionLooksLike to look more closely at how users talk about their depressive symptoms on Twitter.

Methods: We captured 3225 original content tweets for the hashtag #MyDepressionLooksLike that circulated in May of 2016. Eliminating public service announcements, spam, and tweets with links to pictures or videos resulted in a total of 1978 tweets. Using qualitative content analysis, we coded the tweets to detect themes.

Results: The content analysis revealed seven themes: dysfunctional thoughts, lifestyle challenges, social struggles, hiding behind a mask, apathy and sadness, suicidal thoughts and behaviors, and seeking relief.

Conclusions: The themes revealed important information about the content of the public messages that people share about depression on Twitter. More research is needed to understand the effects of the hashtag on increasing social support for users and reducing social stigma related to depression.

Citing Articles

Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study.

Chandrasekaran R, Kotaki S, Nagaraja A Npj Ment Health Res. 2024; 3(1):62.

PMID: 39643656 PMC: 11624259. DOI: 10.1038/s44184-024-00107-5.

How Does an Online Mental Health Community on Twitter Empower Diverse Population Levels and Groups? A Qualitative Analysis of #BipolarClub.

AbouWarda H, Dolata M, Schwabe G J Med Internet Res. 2024; 26:e55965.

PMID: 39158945 PMC: 11369525. DOI: 10.2196/55965.

"Anxiety is not cute" analysis of twitter users' discourses on romanticizing mental illness.

Issaka B, Aidoo E, Wood S, Mohammed F BMC Psychiatry. 2024; 24(1):221.

PMID: 38515062 PMC: 10956207. DOI: 10.1186/s12888-024-05663-w.

Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities.

Aldkheel A, Zhou L J Healthc Inform Res. 2024; 8(1):88-120.

PMID: 38273983 PMC: 10805697. DOI: 10.1007/s41666-023-00152-3.

Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study.

Carabot F, Donat-Vargas C, Santoma-Vilaclara J, Ortega M, Garcia-Montero C, Fraile-Martinez O J Med Internet Res. 2023; 25:e45660.

PMID: 37962927 PMC: 10685273. DOI: 10.2196/45660.

References

Davila J, Hershenberg R, Feinstein B, Gorman K, Bhatia V, Starr L . Frequency and Quality of Social Networking Among Young Adults: Associations With Depressive Symptoms, Rumination, and Corumination. Psychol Pop Media Cult. 2014; 1(2):72-86. PMC: 3907111. DOI: 10.1037/a0027512. View

Whitehead L . Methodological and ethical issues in Internet-mediated research in the field of health: an integrated review of the literature. Soc Sci Med. 2007; 65(4):782-91. DOI: 10.1016/j.socscimed.2007.03.005. View

Cacioppo J, Hughes M, Waite L, Hawkley L, Thisted R . Loneliness as a specific risk factor for depressive symptoms: cross-sectional and longitudinal analyses. Psychol Aging. 2006; 21(1):140-51. DOI: 10.1037/0882-7974.21.1.140. View

Slavich G, ODonovan A, Epel E, Kemeny M . Black sheep get the blues: a psychobiological model of social rejection and depression. Neurosci Biobehav Rev. 2010; 35(1):39-45. PMC: 2926175. DOI: 10.1016/j.neubiorev.2010.01.003. View

Chou H, 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. 2011; 15(2):117-21. DOI: 10.1089/cyber.2011.0324. View

Moreno M, Jelenchick L, Egan K, Cox E, Young H, Gannon K . Feeling bad on Facebook: depression disclosures by college students on a social networking site. Depress Anxiety. 2011; 28(6):447-55. PMC: 3110617. DOI: 10.1002/da.20805. View

Carhart-Harris R, Bolstridge M, Rucker J, Day C, Erritzoe D, Kaelen M . Psilocybin with psychological support for treatment-resistant depression: an open-label feasibility study. Lancet Psychiatry. 2016; 3(7):619-27. DOI: 10.1016/S2215-0366(16)30065-7. View

Brinker J, Cheruvu V . Social and emotional support as a protective factor against current depression among individuals with adverse childhood experiences. Prev Med Rep. 2016; 5:127-133. PMC: 5156603. DOI: 10.1016/j.pmedr.2016.11.018. View

Radovic A, Gmelin T, Stein B, Miller E . Depressed adolescents' positive and negative use of social media. J Adolesc. 2016; 55:5-15. PMC: 5485251. DOI: 10.1016/j.adolescence.2016.12.002. View

10.

Joormann J, Gotlib I . Updating the contents of working memory in depression: interference from irrelevant negative material. J Abnorm Psychol. 2008; 117(1):182-92. DOI: 10.1037/0021-843X.117.1.182. View

11.

Lin L, Sidani J, Shensa A, Radovic A, Miller E, Colditz J . ASSOCIATION BETWEEN SOCIAL MEDIA USE AND DEPRESSION AMONG U.S. YOUNG ADULTS. Depress Anxiety. 2016; 33(4):323-31. PMC: 4853817. DOI: 10.1002/da.22466. View

12.

Kondracki N, Wellman N, Amundson D . Content analysis: review of methods and their applications in nutrition education. J Nutr Educ Behav. 2002; 34(4):224-30. DOI: 10.1016/s1499-4046(06)60097-3. View

13.

Berry N, Lobban F, Belousov M, Emsley R, Nenadic G, Bucci S . #WhyWeTweetMH: Understanding Why People Use Twitter to Discuss Mental Health Problems. J Med Internet Res. 2017; 19(4):e107. PMC: 5399219. DOI: 10.2196/jmir.6173. View

14.

Cheng Q, Li H, Silenzio V, Caine E . Suicide contagion: a systematic review of definitions and research utility. PLoS One. 2014; 9(9):e108724. PMC: 4178222. DOI: 10.1371/journal.pone.0108724. View

15.

Shepherd A, Sanders C, Doyle M, Shaw J . Using social media for support and feedback by mental health service users: thematic analysis of a twitter conversation. BMC Psychiatry. 2015; 15:29. PMC: 4337200. DOI: 10.1186/s12888-015-0408-y. View

16.

Hames J, Hagan C, Joiner T . Interpersonal processes in depression. Annu Rev Clin Psychol. 2013; 9:355-77. DOI: 10.1146/annurev-clinpsy-050212-185553. View

17.

Jelenchick L, Eickhoff J, Moreno M . "Facebook depression?" social networking site use and depression in older adolescents. J Adolesc Health. 2012; 52(1):128-30. DOI: 10.1016/j.jadohealth.2012.05.008. View

18.

Mueller A, Abrutyn S . Suicidal disclosures among friends: using social network data to understand suicide contagion. J Health Soc Behav. 2015; 56(1):131-48. PMC: 4472458. DOI: 10.1177/0022146514568793. View

19.

Barney L, Griffiths K, Jorm A, Christensen H . Stigma about depression and its impact on help-seeking intentions. Aust N Z J Psychiatry. 2006; 40(1):51-4. DOI: 10.1080/j.1440-1614.2006.01741.x. View

20.

Hingle M, Yoon D, Fowler J, Kobourov S, Schneider M, Falk D . Collection and visualization of dietary behavior and reasons for eating using Twitter. J Med Internet Res. 2013; 15(6):e125. PMC: 3713881. DOI: 10.2196/jmir.2613. View