Personalized Prediction of Suicide Risk for Web-based Intervention

Across the United States, suicide is the second leading cause of death for people aged between 15 and 34, and younger people are more prone to mental health problems, suicidal thoughts, and behaviors. For instance, 80% of patients with Borderline Personality Disorder have suicide-related behaviors, and between 4-9% of them commit suicide. Moreover, the social stigma associated with mental health issues and suicide deter patients from sharing their experiences directly with others. In such a situation, social media that provides a free and open forum for voluntary expression can provide insights into suicide ideation and self-destructive behavior. Reddit is a widely used and highly relevant social-media platform where users subscribe to specific subreddits and share their experiences. The users on the respective subreddits often make use of metaphoric suicidal language with related intentions, while interacting with other like-minded users sharing similar experiences. The Columbia-Suicide Severity Rating Scale (C-SSRS) has been employed by clinicians to measure the level of suicidal risk but has not been adequately personalized for improved prevention and resiliency. In this study, we develop a framework for the prediction of suicidal risk by conducting a user-level analysis supervised by C-SSRS and using medical knowledge bases. This will eventually facilitate a clinician to perform a personalized web-based intervention. Our two-fold approach creates a user-level decision-making mechanism that factors in the linguistic, temporal, homophily-based, metaphorical, and intent-based information from the dialogues of 93K users interacting on r/SuicideWatch and other related subreddits that aid in the characterization of users’ suicidal vulnerability