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

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

The Language of Social Support in Social Media and Its Effect on Suicidal Ideation Risk

Online social support is known to play a significant role in mental well-being. However, current research is limited in its ability to quantify this link. Challenges exist due to the paucity of longitudinal, pre- and post mental illness risk data, and reliable methods that can examine causality between past availability of support and future risk. In this paper, we propose a method to measure how the language of comments in Reddit mental health communities influences risk to suicidal ideation in the future. Incorporating human assessments in a stratified propensity score analysis based framework, we identify comparable subpopulations of individuals and measure the effect of online social support language. We interpret these linguistic cues with an established theoretical model of social support, and find that esteem and network support play a more prominent role in reducing forthcoming risk. We discuss the implications of our work for designing tools that can improve support provisions in online communities

Discussion Graphs: Putting Social Media Analysis in Context

Much research has focused on studying complex phenomena through their reflection in social media, from drawing neighborhood boundaries to inferring relationships between medicines and diseases. While it is generally recognized in the social sciences that such studies should be conditioned on gender, time and other confounding factors, few of the studies that attempt to extract information from social media actually condition on such factors due to the difficulty in extracting these factors from naturalistic data and the added complexity of of including them in analyses. In this paper, we present a simple framework for specifying and implementing common social media analyses that makes it trivial to inspect and condition on contextual information. Our data model---discussion graphs---captures both the structural features of relationships inferred from social media as well as the context of the discussions from which they are derived, such as who is participating in the discussions, when and where the discussions are occurring, and what else is being discussed in conjunction. We implement our framework in a tool called DGT, and present case studies on its use. In particular, we show how analyses of neighborhoods and their boundaries based on geo-located social media data can have drastically varying results when conditioned on gender and time

A Social Media Study on the Effects of Psychiatric Medication Use

Understanding the effects of psychiatric medications during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many trials suffer from a lack of generalizability to broader populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to first assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Then, based on a stratified propensity score based causal analysis, we observe that use of specific drugs are associated with characteristic changes in an individual’s psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics