Charles-Edouard Notredame1,2,3,4, M Morgiève5,6,7,8, F Morel9, S Berrouiguet5,10, J Azé11, G Vaiva9,12,5. 1. Psychiatry Department, CHU Lille, 2 rue André Verhaeghe, F-59000, Lille, France. charles-edouard.notredame@chru-lille.fr. 2. SCALab, CNRS UMR9193, F-59000, Lille, France. charles-edouard.notredame@chru-lille.fr. 3. Groupement d'Étude et de Prévention du Suicide, Saint-Benoît, France. charles-edouard.notredame@chru-lille.fr. 4. Papageno Program, Lille, France. charles-edouard.notredame@chru-lille.fr. 5. Groupement d'Étude et de Prévention du Suicide, Saint-Benoît, France. 6. Papageno Program, Lille, France. 7. Centre de Recherche Médecine, Sciences, Santé, Santé Mentale, Société (CERMES3), UMR CNRS 8211-Unité Inserm 988-EHESS-Université Paris Descartes, 75006, Paris, France. 8. Hôpital de la Pitié-Salpêtrière, ICM - Brain and Spine Institute, 47-83, boulevard de l'hôpital, 75013, Paris, France. 9. Psychiatry Department, CHU Lille, 2 rue André Verhaeghe, F-59000, Lille, France. 10. Centre Hospitalier Régional Universitaire de Brest à Bohars, Pôle de psychiatrie, 29820, Bohars, France. 11. LIRMM, UMR 5506, Montpellier University/CNRS, 860 rue de St Priest, 34095, Montpellier Cedex 5, France. 12. SCALab, CNRS UMR9193, F-59000, Lille, France.
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.
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.
Entities:
Keywords:
Affective disorders; Depression; Distress; Social media; Suicidal behaviors
Authors: John W Ayers; Benjamin M Althouse; Eric C Leas; Mark Dredze; Jon-Patrick Allem Journal: JAMA Intern Med Date: 2017-10-01 Impact factor: 21.873