Current Psychiatry Reports

, 21:98 | Cite as

Distress, Suicidality, and Affective Disorders at the Time of Social Networks

  • Charles-Edouard NotredameEmail author
  • M. Morgiève
  • F. Morel
  • S. Berrouiguet
  • J. Azé
  • G. Vaiva
Mood Disorders (E Baca-Garcia, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Mood Disorders


Purpose of Review

We reviewed how scholars recently addressed the complex relationship that binds distress, affective disorders, and suicidal behaviors on the one hand and social networking on the other. We considered the latest machine learning performances in detecting affective-related outcomes from social media data, and reviewed understandings of how, why, and with what consequences distressed individuals use social network sites. Finally, we examined how these insights may concretely instantiate on the individual level with a qualitative case series.

Recent Findings

Machine learning classifiers are progressively stabilizing with moderate to high performances in detecting affective-related diagnosis, symptoms, and risks from social media linguistic markers. Qualitatively, such markers appear to translate ambivalent and socially constrained motivations such as self-disclosure, passive support seeking, and connectedness reinforcement.


Binding data science and psychosocial research appears as the unique condition to ground a translational web-clinic for treating and preventing affective-related issues on social media.


Social media Affective disorders Depression Suicidal behaviors Distress 



Authors want to acknowledge Estelle Saint-Paul and Damien Scliffet for their contribution in the coding procedure.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.

Human and Animal Rights and Informed Consent

Informed consent was obtained from all individual participants included in the study.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Boyd Danah M, Ellison NB. Social network sites: definition, history, and scholarship. Journal of Computer-Mediated Communication. 2007;13:210–30.CrossRefGoogle Scholar
  2. 2.
    Statista. Social media statistics & facts [Internet]. Statista.comv. Available from:
  3. 3.
    Radovic A, Gmelin T, Stein BD, Miller E. Depressed adolescents’ positive and negative use of social media. J Adolesc. 2017;55:5–15.CrossRefGoogle Scholar
  4. 4.
    • Baker DA, Algorta GP. The relationship between online social networking and depression: a systematic review of quantitative studies. Cyberpsychology, Behavior, and Social Networking. 2016;19:638–48. This qualitative study offers interesting insights in the variety of subjective motivations and consequences of using social networks in depressed adolescents. CrossRefGoogle Scholar
  5. 5.
    •• O’Keeffe GS, Clarke-Pearson K, Council on Communications and Media. The impact of social media on children, adolescents, and families. Pediatrics. 2011;127:800–4. With a sound methodology, this exhaustive overview brings light to the complex relationship between social network use and depression. CrossRefGoogle Scholar
  6. 6.
    Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. Problematic social media use: results from a large-scale nationally representative adolescent sample. Jiménez-Murcia S, editor. PLOS ONE. 2017;12:e0169839.CrossRefGoogle Scholar
  7. 7.
    Berryman C, Ferguson CJ, Negy C. Social media use and mental health among young adults. Psychiatry Q. 2018;89:307–14.CrossRefGoogle Scholar
  8. 8.
    Shensa A, Escobar-Viera CG, Sidani JE, Bowman ND, Marshal MP, Primack BA. Problematic social media use and depressive symptoms among U.S. young adults: a nationally-representative study. Social Science & Medicine. 2017;182:150–7.CrossRefGoogle Scholar
  9. 9.
    • Huang C. Time spent on social network sites and psychological well-being: a meta-analysis. Cyberpsychology, Behavior, and Social Networking. 2017;20:346–54. This large-scale inquiry provides strong evidence about the relationship between social media use and depressive symptoms. Google Scholar
  10. 10.
    • Andreassen CS, Pallesen S. Social network site addiction - an overview. Current Pharmaceutical Design. 2014;20:4053–61. This study provides a synthetic overview of the relationship between time spent on social networking sites and psychological well-being factors. CrossRefGoogle Scholar
  11. 11.
    Sussman N, DeJong S. Ethical considerations for mental health clinicians working with adolescents in the digital age. Current Psychiatry Reports. 2018;20:113.CrossRefGoogle Scholar
  12. 12.
    Seabrook EM, Kern ML, Rickard NS. Social networking sites, depression, and anxiety: a systematic review. JMIR Mental Health. 2016;3:e50.CrossRefGoogle Scholar
  13. 13.
    Ernala SK, Rizvi AF, Birnbaum ML, Kane JM, De Choudhury M. Linguistic markers indicating therapeutic outcomes of social media disclosures of schizophrenia. Proceedings of the ACM on Human-Computer Interaction. 2017;1:1–27.CrossRefGoogle Scholar
  14. 14.
    Hswen Y, Naslund JA, Brownstein JS, Hawkins JB. Monitoring online discussions about suicide among Twitter users with schizophrenia: exploratory study. JMIR Mental Health. 2018;5:e11483.CrossRefGoogle Scholar
  15. 15.
    Guntuku SC, Ramsay JR, Merchant RM, Ungar LH. Language of ADHD in adults on social media. J Atten Disord 2017; Scholar
  16. 16.
    Guntuku SC, Lin W, Carpenter J, Ng WK, Ungar LH, Preoţiuc-Pietro D. Studying personality through the content of posted and liked images on Twitter. Proceedings of the 2017 ACM on Web Science Conference - WebSci ’17. Troy, New York, USA: ACM Press; 2017. p. 223–7.Google Scholar
  17. 17.
    Guntuku SC, Buffone A, Jaidka K, Eichstaedt J, Ungar LH. Understanding and measuring psychological stress using social media. In Proceedings of the International AAAI Conference on Web and Social Media. 2019;13(1):214–225.Google Scholar
  18. 18.
    • Cavazos-Rehg PA, Krauss MJ, Sowles SJ, Connolly S, Rosas C, Bharadwaj M, et al. An analysis of depression, self-harm, and suicidal ideation content on Tumblr. Crisis. 2017;38:44–52. This paper represents an important translational effort to derive populational machine learning prediction about stress on social network from individual-level machine learning findings. CrossRefGoogle Scholar
  19. 19.
    Bazarova NN, Choi YH, Whitlock J, Cosley D, Sosik V. Psychological distress and emotional expression on Facebook. Cyberpsychol Behav Soc Netw. 2017;20:157–63.CrossRefGoogle Scholar
  20. 20.
    • Cheng Q, Li TM, Kwok CL, Zhu T, Yip PS. Assessing suicide risk and emotional distress in Chinese social media: a text mining and machine learning study. J Med Internet Res. 2017;19(7):e243. This study offers a noteworthy glimpse in the online behaviors and subjective appraisals of Facebook interactions in distressed adolescents. Google Scholar
  21. 21.
    O’Dea B, Larsen ME, Batterham PJ, Calear AL, Christensen H. A linguistic analysis of suicide-related Twitter posts. Crisis. 2017;38:319–29.CrossRefGoogle Scholar
  22. 22.
    Coppersmith G, Ngo K, Leary R, Wood A. Exploratory analysis of social media prior to a suicide attempt. Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology. San Diego: Association for Computational Linguistics; 2016. p. 106–17.Google Scholar
  23. 23.
    Dasgupta R. The first social media suicide. The Guardian [Internet]. 2017 Aug 29; Available from:
  24. 24.
    Wongkoblap A, Vadillo MA, Curcin V. Researching mental health disorders in the era of social media: systematic review. J Med Internet Res. 2017;19:e228.CrossRefGoogle Scholar
  25. 25.
    Lv M, Li A, Liu T, Zhu T. Creating a Chinese suicide dictionary for identifying suicide risk on social media. PeerJ. 2015;3:e1455.CrossRefGoogle Scholar
  26. 26.
    Guntuku SC, Yaden DB, Kern ML, Ungar LH, Eichstaedt JC. Detecting depression and mental illness on social media: an integrative review. Curr Opin Behav Sci. 2017;18:43–9.CrossRefGoogle Scholar
  27. 27.
    •• Burnap P, Colombo G, Amery R, Hodorog A, Scourfield J. Multi-class machine classification of suicide-related communication on Twitter. Online Social Networks and Media. 2017;2:32–44. This paper presents in a very didactic way the up-to-date evidence about machine learning detection as applied to mental health disorders, and more specifically depression. CrossRefGoogle Scholar
  28. 28.
    Song J, Song TM, Seo D-C, Jin JH. Data mining of web-based documents on social networking sites that included suicide-related words among Korean adolescents. J Adolesc Health. 2016;59:668–73.CrossRefGoogle Scholar
  29. 29.
    Luo J, Du J, Tao C, Xu H, Zhang Y. Exploring temporal suicidal behavior patterns on social media: insight from Twitter analytics. Health Informatics J, 2019;
  30. 30.
    • Kahn JH, Garrison AM. Emotional self-disclosure and emotional avoidance: relations with symptoms of depression and anxiety. Journal of Counseling Psychology. 2009;56:573–84. This study offers a promising prospect on how to improve suicidal risk detection by integrating dynamic time variants. CrossRefGoogle Scholar
  31. 31.
    Robert A, Suelves JM, Armayones M, Ashley S. Internet use and suicidal behaviors: internet as a threat or opportunity? Telemedicine and e-Health. 2015;21:306–11.CrossRefGoogle Scholar
  32. 32.
    Ophir Y. SOS on SNS: adolescent distress on social network sites. Comput Hum Behav. 2017;68:51–5.CrossRefGoogle Scholar
  33. 33.
    Monteith S, Glenn T. Automated decision-making and big data: concerns for people with mental illness. Curr Psychiatry Rep. 2016;18:112.CrossRefGoogle Scholar
  34. 34.
    •• Park J, Lee DS, Shablack H, Verduyn P, Deldin P, Ybarra O, et al. When perceptions defy reality: the relationships between depression and actual and perceived Facebook social support. Journal of Affective Disorders. 2016;200:37–44. With an ad hoc methodology, Park et al. provide an unprecedented insight in the dissociation between reception and perception of online support in depressed people. CrossRefGoogle Scholar
  35. 35.
    Tamir DI, Mitchell JP. Anchoring and adjustment during social inferences. J Exp Psychol Gen. 2013;142:151–62.CrossRefGoogle Scholar
  36. 36.
    Suler J. The online disinhibition effect. Cyber Psychology & Behavior. 2004;7:321–6.CrossRefGoogle Scholar
  37. 37.
    Hindmarch T, Hotopf M, Owen GS. Depression and decision-making capacity for treatment or research: a systematic review. BMC Med Ethics. 2013;14:54.CrossRefGoogle Scholar
  38. 38.
    Jollant F, Bellivier F, Leboyer M, Astruc B, Torres S, Verdier R, et al. Impaired decision making in suicide attempters. Am J Psychiatr. 2005;162:304–10.CrossRefGoogle Scholar
  39. 39.
    Dombrovski AY, Hallquist MN. The decision neuroscience perspective on suicidal behavior: evidence and hypotheses. Current Opinion in Psychiatry. 2017;30:7–14.CrossRefGoogle Scholar
  40. 40.
    Frison E, Subrahmanyam K, Eggermont S. The short-term longitudinal and reciprocal relations between peer victimization on Facebook and adolescents’ well-being. Journal of Youth and Adolescence. 2016;45:1755–71.CrossRefGoogle Scholar
  41. 41.
    •• Seward A-L, Harris KM. Offline versus online suicide-related help seeking: changing domains, changing paradigms: offline versus online. Journal of Clinical Psychology. 2016;72:606–20. The results of this survey illustrate how the Internet has altered the classical help-seeking paradigm in relation with suicide behaviors. CrossRefGoogle Scholar
  42. 42.
    • Chan M, TMH L, Law YW, Wong PWC, Chau M, Cheng C, et al. Engagement of vulnerable youths using internet platforms. van Amelsvoort T, editor. PLOS ONE. 2017;12:e0189023. This survey disentangles the factors associated with online expression of distress, depending on whether individuals formal seek help. Google Scholar
  43. 43.
    Joiner TE, Metalsky GI, Katz J, Beach SRH. Depression and excessive reassurance-seeking. Psychol Inq. 1999;10:269–78.CrossRefGoogle Scholar
  44. 44.
    Ybarra ML, Mitchell KJ, Palmer NA, Reisner SL. Online social support as a buffer against online and offline peer and sexual victimization among U.S. LGBT and non-LGBT youth. Child Abuse & Neglect. 2015;39:123–36.CrossRefGoogle Scholar
  45. 45.
    •• Ridout B, Campbell A. The use of social networking sites in mental health interventions for young people: systematic review. Journal of Medical Internet Research. 2018;20:e12244. This review provides a unique perspective on available evidence about the effectiveness, suitability, and safety of online interventions to support the young people’s mental health. CrossRefGoogle Scholar
  46. 46.
    Beck AT. Coginitive therapy and the emotional disorders. New York Meridian. New-York; 1976.Google Scholar
  47. 47.
    Gotlib IH, Krasnoperova E, Yue DN, Joormann J. Attentional biases for negative interpersonal stimuli in clinical depression. J Abnorm Psychol. 2004;113:127–35.CrossRefGoogle Scholar
  48. 48.
    Rideout V. Social media, social life: how teens view their digital lives. Common Sense Media 2012.
  49. 49.
    Moreno MA, Jelenchick LA, Egan KG, Cox E, Young H, Gannon KE, et al. Feeling bad on Facebook: depression disclosures by college students on a social networking site. Depress Anxiety. 2011;28:447–55.CrossRefGoogle Scholar
  50. 50.
    •• Colombo GB, Burnap P, Hodorog A, Scourfield J. Analysing the connectivity and communication of suicidal users on Twitter. Computer Communications. 2016;73:291–300. This study reveals the functional and structural properties of suicidal Twitter users’ networks, with major comprehensive and translational implications. CrossRefGoogle Scholar
  51. 51.
    Joiner TE. The clustering and contagion of suicide. Curr Dir Psychol Sci. 1999;8:89–92.CrossRefGoogle Scholar
  52. 52.
    Ehrenreich SE, Underwood MK. Adolescents’ internalizing symptoms as predictors of the content of their Facebook communication and responses received from peers. Translational Issues in Psychological Science. 2016;2:227–37.CrossRefGoogle Scholar
  53. 53.
    Schwartz-Mette RA, Rose AJ. Co-rumination mediates contagion of internalizing symptoms within youths’ friendships. Dev Psychol. 2012;48:1355–65.CrossRefGoogle Scholar
  54. 54.
    Whitlock J, Wyman PA, Moore SR. Connectedness and suicide prevention in adolescents: pathways and implications. Suicide Life Threat Behav. 2014;44:246–72.CrossRefGoogle Scholar
  55. 55.
    Santini ZI. The impact of social networks and social support on mental disorders and mortality. [Barcelona]: Universitat de Barcelona; 2016.Google Scholar
  56. 56.
    Hobbs WR, Burke M, Christakis NA, Fowler JH. Online social integration is associated with reduced mortality risk. Proc Natl Acad Sci. 2016;113:12980–4.CrossRefGoogle Scholar
  57. 57.
    Mesoudi A. The cultural dynamics of copycat suicide. Jones JH, editor. PLoS ONE. 2009;4:e7252.CrossRefGoogle Scholar
  58. 58.
    Kramer A, Guillory J, Hancock J. Experimental evidence of massive-scale emotional contagion through social networks. PNAS. 2014;111:8788–90.CrossRefGoogle Scholar
  59. 59.
    Daine K, Hawton K, Singaravelu V, Stewart A, Simkin S, Montgomery P. The power of the web: a systematic review of studies of the influence of the internet on self-harm and suicide in young people. García AV, editor. PLoS ONE. 2013;8:e77555.CrossRefGoogle Scholar
  60. 60.
    Ayers JW, Althouse BM, Leas EC, Dredze M, Allem J-P. Internet searches for suicide following the release of 13 Reasons Why. JAMA Intern Med. 2017;177:1527–9.CrossRefGoogle Scholar
  61. 61.
    Ueda M, Mori K, Matsubayashi T, Sawada Y. Tweeting celebrity suicides: users’ reaction to prominent suicide deaths on Twitter and subsequent increases in actual suicides. Soc Sci Med. 2017;189:158–66.CrossRefGoogle Scholar
  62. 62.
    •• Scatà M, Di Stefano A, La Corte A, Liò P. Quantifying the propagation of distress and mental disorders in social networks. Scientific Reports. 2018;8:5005. Scatà et al. provide unprecedented computational evidence that overlapping awareness can accelerate of delay the contagion of distress symptoms within a social network. This has direct and major implication for universal prevention. Google Scholar
  63. 63.
    Notredame C-E, Grandgenèvre P, Pauwels N, Morgiève M, Wathelet M, Vaiva G, et al. Leveraging the web and social media to promote access to care among suicidal individuals. Front Psychol. 2018;9:1338.CrossRefGoogle Scholar
  64. 64.
    Tan Z, Liu X, Liu X, Cheng Q, Zhu T. Designing microblog direct messages to engage social media users with suicide ideation: interview and survey study on Weibo. J Med Internet Res. 2017;19:e381.CrossRefGoogle Scholar
  65. 65.
    •• Robinson J, Cox G, Bailey E, Hetrick S, Rodrigues M, Fisher S, et al. Social media and suicide prevention: a systematic review: suicide prevention and social media. Early Intervention in Psychiatry. 2016;10:103–21. This review lays a solid conceptual and empirical ground for suicide prevention on social media. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Charles-Edouard Notredame
    • 1
    • 2
    • 3
    • 4
    Email author
  • M. Morgiève
    • 3
    • 4
    • 5
    • 6
  • F. Morel
    • 1
  • S. Berrouiguet
    • 3
    • 7
  • J. Azé
    • 8
  • G. Vaiva
    • 1
    • 2
    • 3
  1. 1.Psychiatry DepartmentCHU LilleLilleFrance
  2. 2.SCALab, CNRS UMR9193LilleFrance
  3. 3.Groupement d’Étude et de Prévention du SuicideSaint-BenoîtFrance
  4. 4.Papageno ProgramLilleFrance
  5. 5.Centre de Recherche Médecine, Sciences, Santé, Santé Mentale, Société (CERMES3), UMR CNRS 8211-Unité Inserm 988-EHESS-Université Paris DescartesParisFrance
  6. 6.Hôpital de la Pitié-SalpêtrièreICM – Brain and Spine InstituteParisFrance
  7. 7.Centre Hospitalier Régional Universitaire de Brest à BoharsPôle de psychiatrieBoharsFrance
  8. 8.LIRMM, UMR 5506Montpellier University/CNRSMontpellier Cedex 5France

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