多智能体因果推理用于通过在线对话检测自杀念头
Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations
AI总结：
本文提出多智能体因果推理框架，通过扩展用户互动和减轻隐藏偏见来提高在线对话中自杀念头检测的准确性。
摘要：
自杀仍然是一个紧迫的全球公共卫生问题。尽管社交媒体平台通过在线对话树提供了早期风险检测的机会，但现有方法面临两个主要局限：（1）它们依赖于预定义的规则（例如引用或依赖）来记录对话，只能捕捉用户互动的狭窄范围；（2）它们忽略了隐藏影响，如用户从众行为和自杀模仿行为，这些因素可能显著影响在线社区中的自杀表达和传播。为了解决这些局限，我们提出了一种多智能体因果推理（MACR）框架，该框架协同使用推理代理来扩展用户互动，并使用偏见-aware决策代理来减轻隐藏影响带来的有害偏见。推理代理整合认知评估理论，生成对帖子的反事实用户反应，从而扩展用户互动。它通过结构化维度分析这些反应，即认知、情感和行为模式，每个维度都有专门的子代理负责。偏见-aware决策代理通过前门调整策略减轻隐藏偏见，利用推理代理生成的反事实用户反应。通过推理和偏见-aware决策的协作，所提出的MACR框架不仅减轻了隐藏偏见，还通过反事实知识丰富了用户互动的上下文信息。在真实世界对话数据集上的广泛实验证明了MACR在识别自杀风险的有效性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
