Natural Language Processing of Social Media as Screening for Suicide Risk

Biomedical Informatics Insights
Glen CoppersmithAlex Fine

Abstract

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people's lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media-and the near-ubiquity of mobile devices used to access social media networks-offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have "opted in" for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially i...Continue Reading

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Feb 13, 2020·Current Opinion in Psychiatry·Janna N de BoerIris E C Sommer
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Software Mentioned

Fitbit
Cambridge Analytica
Jawbone
Runkeeper
Strava
Crisis Text Line ( CTL
OurDataHelps

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