Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media. Shimgekar, S. R., Zhao, R., Goyal, A., Rodriguez, V. J., Bloom, P., Kumar, N., Sundaram, H., & Saha, K. In Proceedings of the 18th ACM Web Science Conference 2026, of WebSci '26, pages 176–187, New York, NY, USA, May, 2026. Association for Computing Machinery.
Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media [link]Paper  doi  abstract   bibtex   
On social media, almost 50-60% of individuals experiencing suicidal ideation (SI) do not disclose their distress explicitly [32]. 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.
@inproceedings{shimgekar_detecting_2026,
	address = {New York, NY, USA},
	series = {{WebSci} '26},
	title = {Detecting {Early} and {Implicit} {Suicidal} {Ideation} via {Longitudinal} and {Information} {Environment} {Signals} on {Social} {Media}},
	isbn = {979-8-4007-2504-3},
	url = {https://dl.acm.org/doi/10.1145/3795766.3799758},
	doi = {10.1145/3795766.3799758},
	abstract = {On social media, almost 50-60\% of individuals experiencing suicidal ideation (SI) do not disclose their distress explicitly [32]. 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.},
	urldate = {2026-06-07},
	booktitle = {Proceedings of the 18th {ACM} {Web} {Science} {Conference} 2026},
	publisher = {Association for Computing Machinery},
	author = {Shimgekar, Soorya Ram and Zhao, Ruining and Goyal, Agam and Rodriguez, Violeta J. and Bloom, Paul and Kumar, Navin and Sundaram, Hari and Saha, Koustuv},
	month = may,
	year = {2026},
	pages = {176--187},
	file = {Full Text PDF:/Users/mexico/Zotero/storage/KN64UJTY/Shimgekar et al. - 2026 - Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals.pdf:application/pdf},
}
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