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Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media.
De Choudhury, Munmun; Kiciman, Emre; Dredze, Mark; Coppersmith, Glen; Kumar, Mrinal.
Afiliación
  • De Choudhury M; Georgia Tech, Atlanta GA 30332.
  • Kiciman E; Microsoft Research, Redmond WA 98052.
  • Dredze M; Johns Hopkins University, Baltimore MD 21218.
  • Coppersmith G; Qntfy.io, Crownsville MD, 21032.
  • Kumar M; Georgia Tech, Atlanta GA 30332.
Proc SIGCHI Conf Hum Factor Comput Syst ; 2016: 2098-2110, 2016 May.
Article en En | MEDLINE | ID: mdl-29082385
History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Proc SIGCHI Conf Hum Factor Comput Syst Año: 2016 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Proc SIGCHI Conf Hum Factor Comput Syst Año: 2016 Tipo del documento: Article