Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
Munmun De Choudhury,Emre Kiciman,Mark Dredze,Glen Coppersmith,Mrinal Kumar +4 more
- Vol. 2016, pp 2098-2110
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TLDR
This paper develops a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation, and utilizes semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts.Abstract:
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.read more
Citations
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Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.
TL;DR: A layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health is provided, focused principally on smartphones, but also including studies of wearables, social media, and computers.
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Detecting depression and mental illness on social media: an integrative review
Sharath Chandra Guntuku,David B. Yaden,Margaret L. Kern,Lyle H. Ungar,Johannes C. Eichstaedt +4 more
TL;DR: Automated detection methods may help to identify depressed or otherwise at-risk individuals through the large-scale passive monitoring of social media, and in the future may complement existing screening procedures.
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Instagram photos reveal predictive markers of depression
TL;DR: Using Instagram data from 166 individuals, machine learning tools were applied to successfully identify markers of depression and human ratings of photo attributes were weaker predictors of depression, and were uncorrelated with computationally-generated features.
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Forecasting the onset and course of mental illness with Twitter data
Andrew Reece,Andrew J. Reagan,Katharina L. M. Lix,Peter Sheridan Dodds,Christopher M. Danforth,Ellen J. Langer +5 more
TL;DR: In this paper, the authors developed computational models to predict the emergence of depression and post-traumatic stress disorder in Twitter users, and compared favorably to general practitioners' average success rates in diagnosing depression.
Proceedings ArticleDOI
Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression
TL;DR: It is found that people use Instagram to engage in social exchange and story-telling about difficult experiences, and personal narratives, food and beverage, references to illness, and self-appearance concerns are more likely to attract positive social support.
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