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

TL;DR

This paper introduces a Multi-Agent Causal Reasoning framework that enhances online suicide ideation detection by modeling complex user interactions and mitigating hidden biases through counterfactual analysis and structured reasoning.

Contribution

It proposes a novel multi-agent framework combining causal reasoning and bias mitigation to improve suicide risk detection in online conversations.

Findings

MACR outperforms existing methods in accuracy and robustness.

Counterfactual reasoning enriches contextual understanding of user interactions.

Bias mitigation reduces false positives and improves fairness.

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.