Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly leveraging a user’s emotional history and social information from a user’s neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, in combination with the Hawkes process to learn the historical emotional spectrum of a user in a time-sensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.

Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Contextualizing the build-up of such ideation is critical for the identification of users at risk. In this work, we focus on identifying suicidal intent in tweets by augmenting linguistic models with emotional phases modeled from users’ historical context. We propose PHASE, a time-and phase-aware framework that adaptively learns features from a user’s historical emotional spectrum on Twitter for preliminary screening of suicidal risk. Building on clinical studies, PHASE learns phase-like progressions in users’ historical Plutchik-wheel-based emotions to contextualize suicidal intent. While outperforming state-of-the-art methods, we show the utility of temporal and phase-based emotional contextual cues for suicide ideation detection. We further discuss practical and ethical considerations.

Social media’s ubiquity fosters a space for users to exhibit suicidal thoughts outside of traditional clinical settings. Understanding the build-up of such ideation is critical for the identification of at-risk users and suicide prevention. Suicide ideation is often linked to a history of mental depression. The emotional spectrum of a user’s historical activity on social media can be indicative of their mental state over time. In this work, we focus on identifying suicidal intent in English tweets by augmenting linguistic models with historical context. We propose STATENet, a time-aware transformer based model for preliminary screening of suicidal risk on social media. STATENet outperforms competitive methods, demonstrating the utility of emotional and temporal contextual cues for suicide risk assessment. We discuss the empirical, qualitative, practical, and ethical aspects of STATENet for suicide ideation detection.