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