De Choudhury Munmun, Kiciman Emre, Dredze Mark, Coppersmith Glen, Kumar Mrinal
Georgia Tech, Atlanta GA 30332.
Microsoft Research, Redmond WA 98052.
Proc SIGCHI Conf Hum Factor Comput Syst. 2016 May;2016:2098-2110. doi: 10.1145/2858036.2858207.
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.
精神疾病史是自杀风险和自杀念头背后的主要因素。然而,由于关于自杀念头的信息匮乏,再加上精神疾病的污名化,对这种风险进行特征描述和预测的研究工作受到限制。本文通过开发一种统计方法来推断哪些个体可能从心理健康话题转变为自杀念头,填补了文献中的空白。我们利用Reddit上的半匿名支持社区作为不引人注目的数据源,来推断这些转变的可能性。我们为此开发了语言和互动测量方法,以及基于倾向得分匹配的统计方法。我们的方法使我们能够得出向自杀念头转变的不同标志。这些标志可以在预测框架中进行建模,以识别未来可能产生自杀念头的个体。我们讨论了这项研究的社会和伦理意义。