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.

/pdf/discovering-shifts-to-suicidal-ideation-from-mental-health-1zoqrfh5o5.pdf

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

https://openresearch-repository.anu.edu.au/bitstream/1885/13644/2/O'dea%20et%20al%20Detecting%20Suicidality%20on%20Twitter%202015.pdf

Detecting suicidality on Twitter

/pdf/shifts-to-suicidal-ideation-from-mental-health-content-in-4s2qrd30t8.pdf

Shifts to Suicidal Ideation from Mental Health Content in Social Media

Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health ‐ much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.

/pdf/from-adhd-to-sad-analyzing-the-language-of-mental-health-on-1ozrwfzutq.pdf

From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses

Background: Suicide is a leading cause of death in the United States. Social media such as Twitter is an emerging surveillance tool that may assist researchers in tracking suicide risk factors in real time. Aims: To identify suicide-related risk factors through Twitter conversations by matching on geographic suicide rates from vital statistics data. Method: At-risk tweets were filtered from the Twitter stream using keywords and phrases created from suicide risk factors. Tweets were grouped by state and departures from expectation were calculated. The values for suicide tweeters were compared against national data of actual suicide rates from the Centers for Disease Control and Prevention. Results: A total of 1,659,274 tweets were analyzed over a 3-month period with 37,717 identified as at-risk for suicide. Midwestern and western states had a higher proportion of suicide-related tweeters than expected, while the reverse was true for southern and eastern states. A strong correlation was observed between sta...

Trenton Argyle

Papers

Tracking Suicide Risk Factors Through Twitter in the US