Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915810565423104 |
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| author | Shimgekar, Soorya Ram Zhao, Ruining Goyal, Agam Rodriguez, Violeta J. Bloom, Paul A. Kumar, Navin Sundaram, Hari Saha, Koustuv |
| author_facet | Shimgekar, Soorya Ram Zhao, Ruining Goyal, Agam Rodriguez, Violeta J. Bloom, Paul A. Kumar, Navin Sundaram, Hari Saha, Koustuv |
| contents | On social media, several 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 an average of 10% over all other 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14889 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media Shimgekar, Soorya Ram Zhao, Ruining Goyal, Agam Rodriguez, Violeta J. Bloom, Paul A. Kumar, Navin Sundaram, Hari Saha, Koustuv Social and Information Networks Artificial Intelligence Computation and Language Computers and Society Human-Computer Interaction On social media, several 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 an average of 10% over all other 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. |
| title | Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media |
| topic | Social and Information Networks Artificial Intelligence Computation and Language Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.14889 |