Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media

TL;DR

This work tackles detecting early and implicit suicidal ideation (SI) on social media by modeling an individual's long-term activity together with the discourse of their socially proximal peers. It introduces a framework combining timeline construction, neighbor detection via a multi-centrality NeighborScore, and fine-tuning of DeBERTa-v3-large to fuse self-content and neighbor interactions into a unified representation. Using Reddit data from r/SuicideWatch with 1,000 users (Case/Control), the study shows that incorporating neighbor posts yields a 15% improvement over models relying solely on personal history, with optimal predictive performance reached when sampling around 7 neighbor posts and 88 of the user's posts. These findings support the relational nature of SI and suggest that social-context signals can enable ethically responsible, context-aware early-warning systems for online risk detection.

Abstract

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.

Figures (4)

• Figure 1: Distribution of SI probability for the same set of users before their last non suicidal post and their first post in r/SuicideWatch.
• Figure 2: Illustration of user interactions (Immediate/Neighbor): Immediate interactions include users’ self-posts, self-comments, and received replies, while neighbor interactions represent top neighbor posts and self-posts. Top neighbors identified via NeighborScore
• Figure 3: Performance metrics on varying the number of input user posts ($t$). The plots show that the performance peaks at t=88.
• Figure 4: Performance metrics on varying the number of neighbor posts ($n$). The plots show that the performance peaks at around $n$=7.