Forecasting the onset and course of mental illness with Twitter data

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners\u27 average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness

"Narco" Emotions: Affect and Desensitization in Social Media during the Mexican Drug War

Social media platforms have emerged as prominent information sharing ecosystems in the context of a variety of recent crises, ranging from mass emergencies, to wars and political conflicts. We study affective responses in social media and how they might indicate desensitization to violence experienced in communities embroiled in an armed conflict. Specifically, we examine three established affect measures: negative affect, activation, and dominance as observed on Twitter in relation to a number of statistics on protracted violence in four major cities afflicted by the Mexican Drug War. During a two year period (Aug 2010-Dec 2012), while violence was on the rise in these regions, our findings show a decline in negative emotional expression as well as a rise in emotional arousal and dominance in Twitter posts: aspects known to be psychological markers of desensitization. We discuss the implications of our work for behavioral health, facilitating rehabilitation efforts in communities enmeshed in an acute and persistent urban warfare, and the impact on civic engagement.Comment: Best paper award at the 32nd annual ACM conference on Human factors in computing systems (CHI '14). ACM, New York, NY, USA, pages 3563-357

Media use and insomnia after terror attacks in France

Direct exposure to traumatic events often precipitates sleep disorders. Sleep disturbance has also been observed amongst those indirectly exposed to trauma, via mass media. However, previous work has focused on traditional media use, rather than contemporary social media. We tested associations between both traditional and social media consumption and insomnia symptoms following 2015 terror attacks in Paris France, controlling for location and post-traumatic symptomology. 1878 respondents, selected to represent the national French population, completed an internet survey a month after the Bataclan attacks (response rate 72%). Respondents indicated different media use, post-traumatic stress and insomnia. Controlling for demographics, location and PTSD, insomnia was associated with both traditional (β 0.10, P = .001) and social media use (β 0.12, P = .001). Associations between social media and insomnia were independent of traditional media use. Interventions targeted at social media may be particularly important following mass trauma