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

• URL: http://arxiv.org/abs/2510.14889v2
• Date: Thu, 30 Oct 2025 16:09:51 GMT
• Title: Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
• Authors: Soorya Ram Shimgekar, Ruining Zhao, Agam Goyal, Violeta J. Rodriguez, Paul A. Bloom, Hari Sundaram, Koustuv Saha,
• Abstract summary: On social media, many individuals experiencing suicidal ideation (SI) do not disclose their distress explicitly.<n>We 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.<n>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.
• Score: 12.559986358231633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
  We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
  arXiv  Detail & Related papers  (2026-01-29T03:53:25Z)
• Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems [29.596401271139797]
  We introduce Retentive Relevance, a novel content-level survey-based feedback measure.<n>Retentive Relevance directly assesses users' intent to return to the platform for similar content.<n>We show that Retentive Relevance significantly outperforms both engagement signals and other survey measures in predicting next-day retention.
  arXiv  Detail & Related papers  (2025-10-08T23:38:57Z)
• Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids [2.721477719641864]
  This paper introduces a graph-centric, multimodal detector that fuses physical-layer and behavioral indicators over temporal windows to detect passive attacks.<n>The model achieves a testing accuracy of 98.32% per-timestep and 93.35% per-sequence at 0.15% FPR.
  arXiv  Detail & Related papers  (2025-09-29T08:52:30Z)
• Mind the Gap! Static and Interactive Evaluations of Large Audio Models [55.87220295533817]
  Large Audio Models (LAMs) are designed to power voice-native experiences.<n>This study introduces an interactive approach to evaluate LAMs and collect 7,500 LAM interactions from 484 participants.
  arXiv  Detail & Related papers  (2025-02-21T20:29:02Z)
• Interpretable Early Warnings using Machine Learning in an Online Game-experiment [0.6827423171182153]
  A large-scale social game, Reddit's r/place, provides a unique opportunity to test statistical early warning signals.<n>We develop a machine-learning-based early warning system that combines the predictive power of multiple system-specific time series.<n>Our algorithm detects half of the transitions occurring within 20 minutes at a false positive rate of just 3.7%.
  arXiv  Detail & Related papers  (2025-02-14T03:14:50Z)
• CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks [0.9553673944187253]
  Advanced Persistent Threats (APTs) represent a significant challenge in cybersecurity.<n>Traditional Intrusion Detection Systems (IDS) often fall short in detecting these multi-stage attacks.
  arXiv  Detail & Related papers  (2025-01-06T12:43:59Z)
• Epidemiology-informed Network for Robust Rumor Detection [59.89351792706995]
  We propose a novel Epidemiology-informed Network (EIN) that integrates epidemiological knowledge to enhance performance.<n>To adapt epidemiology theory to rumor detection, it is expected that each users stance toward the source information will be annotated.<n>Our experimental results demonstrate that the proposed EIN not only outperforms state-of-the-art methods on real-world datasets but also exhibits enhanced robustness across varying tree depths.
  arXiv  Detail & Related papers  (2024-11-20T00:43:32Z)
• Modeling User Preferences via Brain-Computer Interfacing [54.3727087164445]
  We use Brain-Computer Interfacing technology to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. We link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
  arXiv  Detail & Related papers  (2024-05-15T20:41:46Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• Flipping the Perspective in Contact Tracing [0.0]
  We introduce a fundamentally different paradigm for contact tracing. For each positive case, do not only ask direct contacts to quarantine; instead, tell everyone how many relationships away the disease just struck. This new approach brings a new tool to bear on pandemic control, powered by network theory.
  arXiv  Detail & Related papers  (2020-10-08T07:17:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.