Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations

• URL: http://arxiv.org/abs/2602.23577v1
• Date: Fri, 27 Feb 2026 01:06:18 GMT
• Title: Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations
• Authors: Jun Li, Xiangmeng Wang, Haoyang Li, Yifei Yan, Shijie Zhang, Hong Va Leong, Ling Feng, Nancy Xiaonan Yu, Qing Li,
• Abstract summary: Suicide remains a pressing global public health concern.<n>Social media platforms offer opportunities for early risk detection through online conversation trees.<n>Existing approaches face two major limitations.
• Score: 16.626899117362875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Suicide remains a pressing global public health concern. While social media platforms offer opportunities for early risk detection through online conversation trees, existing approaches face two major limitations: (1) They rely on predefined rules (e.g., quotes or relies) to log conversations that capture only a narrow spectrum of user interactions, and (2) They overlook hidden influences such as user conformity and suicide copycat behavior, which can significantly affect suicidal expression and propagation in online communities. To address these limitations, we propose a Multi-Agent Causal Reasoning (MACR) framework that collaboratively employs a Reasoning Agent to scale user interactions and a Bias-aware Decision-Making Agent to mitigate harmful biases arising from hidden influences. The Reasoning Agent integrates cognitive appraisal theory to generate counterfactual user reactions to posts, thereby scaling user interactions. It analyses these reactions through structured dimensions, i.e., cognitive, emotional, and behavioral patterns, with a dedicated sub-agent responsible for each dimension. The Bias-aware Decision-Making Agent mitigates hidden biases through a front-door adjustment strategy, leveraging the counterfactual user reactions produced by the Reasoning Agent. Through the collaboration of reasoning and bias-aware decision making, the proposed MACR framework not only alleviates hidden biases, but also enriches contextual information of user interactions with counterfactual knowledge. Extensive experiments on real-world conversational datasets demonstrate the effectiveness and robustness of MACR in identifying suicide risk.

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