Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
This work addresses the problem of identifying indirect suicide risk signals for mental health monitoring, though it is incremental in applying existing methods to this specific domain.
The paper tackles the challenge of detecting early and implicit suicidal ideation (SI) on social media by developing a computational framework that models users' longitudinal posting histories and peer interactions, achieving a 15% improvement over individual-only baselines in a Reddit study with 1,000 users.
On social media, many individuals experiencing suicidal ideation (SI) do not disclose their distress explicitly. Instead, signs may surface indirectly through everyday posts or peer interactions. Detecting such implicit signals early is critical but remains challenging. We frame early and implicit SI as a forward-looking prediction task and develop a computational framework that models a user's information environment, consisting of both their longitudinal posting histories as well as the discourse of their socially proximal peers. We adopted a composite network centrality measure to identify top neighbors of a user, and temporally aligned the user's and neighbors' interactions -- integrating the multi-layered signals in a fine-tuned DeBERTa-v3 model. In a Reddit study of 1,000 (500 Case and 500 Control) users, our approach improves early and implicit SI detection by 15% over individual-only baselines. These findings highlight that peer interactions offer valuable predictive signals and carry broader implications for designing early detection systems that capture indirect as well as masked expressions of risk in online environments.