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

Jun Li, Xiangmeng Wang, Haoyang Li, Yifei Yan, Shijie Zhang, Hong Va Leong, Ling Feng, Nancy Xiaonan Yu, Qing Li

TL;DR

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 is proposed.

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.

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

TL;DR

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 is proposed.

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.
Paper Structure (22 sections, 13 equations, 3 figures, 3 tables)

This paper contains 22 sections, 13 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Suicide Risk Detection on Social Media
  4. Preliminaries
  5. Structural Causal Model
  6. Front-door Adjustment
  7. Causal Analysis and Motivations
  8. Causal Analysis
  9. Motivations
  10. Agent-based Decomposition of Front-door Adjustment
  11. Method
  12. Reasoning Agent: Scalable Counterfactual Reaction Generator
  13. Bias-aware Decision-making Agent
  14. Experiments
  15. Experimental Dataset
  16. ...and 7 more sections

Figures (3)

  • Figure 1: (a) Example conversation tree from "r/SuicideWatch" in Reddit.(2) The limitations of current conversation tree-based methods
  • Figure 2: (a) The causality of conversation tree variable $X$ and suicide risk variable $Y$ is confounded by unobservable variable $U$. (b) The psychological inference generated by Reasoning Agent as a mediator variable $M$ between conversation tree variable $X$ and suicide risk variable $Y$.
  • Figure 3: Overview of Multi-Agent Causal Reasoning (MACR) framework.

Theorems & Definitions (2)

  • definition 1: Causal Graph
  • definition 2: Confounder