Citation

Notredame CÉ, Morgiève M, Morel F, Berrouiguet S, Aze J, Vaiva G. Curr. Psychiatry Rep. 2019; 21(10): e98.

Copyright

(Copyright © 2019, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11920-019-1087-z

PMID

31522268

Abstract

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.

Language: en

Keywords

Humans; Depression; Suicide; Mood Disorders; Social Support; Suicidal Ideation; Suicide Prevention; Suicidal behaviors; Distress; Affective disorders; Social media; Social Media; Social Networking; Machine Learning; Internet-Based Intervention