Towards Suicide Ideation Detection Through Online Conversational Context

Abstract

Social media enable users to share their feelings and emotional struggles. They also offer an opportunity to provide community support to suicidal users. Recent studies on suicide risk assessment have explored the user's historic timeline and information from their social network to analyze their emotional state. However, such methods often require a large amount of user-centric data. A less intrusive alternative is to only use conversation trees arising from online community responses. Modeling such online conversations between the community and a person in distress is an important context for understanding that person's mental state. However, it is not trivial to model the vast number of conversation trees on social media, since each comment has a diverse influence on a user in distress. Typically, a handful of comments/posts receive a significantly high number of replies, which results in scale-free dynamics in the conversation tree. Moreover, psychological studies suggested that it is important to capture the fine-grained temporal irregularities in the release of vast volumes of comments, since suicidal users react quickly to online community support. Building on these limitations and psychological studies, we propose HCN, a Hyperbolic Conversation Network, which is a less user-intrusive method for suicide ideation detection. HCN leverages the hyperbolic space to represent the scale-free dynamics of online conversations. Through extensive quantitative, qualitative, and ablative experiments on real-world Twitter data, we find that HCN outperforms state-of-the art methods, while using 98% less user-specific data, and while maintaining a 74% lower carbon footprint and a 94% smaller model size. We also find that the comments within the first half an hour are most important to identify at-risk users.