Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning

By Ramit Sawhney, Harshit Joshi, Rajiv Ratn Shah, Lucie Flek. 2021

60 citations recorded by the source corpus.

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.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Existing work has looked into detecting suicidal ideation from users on social media, such as in Sawhney et al. (2021).”