Exploring Health Misinformation Detection with Multi-Agent Debate

• URL: http://arxiv.org/abs/2512.09935v1
• Date: Sat, 29 Nov 2025 12:39:30 GMT
• Title: Exploring Health Misinformation Detection with Multi-Agent Debate
• Authors: Chih-Han Chen, Chen-Han Tsai, Yu-Shao Peng,
• Abstract summary: We propose a two-stage framework for health misinformation detection.<n>In the first stage, we employ large language models (LLMs) to independently evaluate retrieved articles.<n>When this score indicates insufficient consensus-falling below a predefined threshold-the system proceeds to a second stage.<n>Multiple agents engage in structured debate to synthesize conflicting evidence and generate well-reasoned verdicts with explicit justifications.
• Score: 0.11470070927586014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fact-checking health-related claims has become increasingly critical as misinformation proliferates online. Effective verification requires both the retrieval of high-quality evidence and rigorous reasoning processes. In this paper, we propose a two-stage framework for health misinformation detection: Agreement Score Prediction followed by Multi-Agent Debate. In the first stage, we employ large language models (LLMs) to independently evaluate retrieved articles and compute an aggregated agreement score that reflects the overall evidence stance. When this score indicates insufficient consensus-falling below a predefined threshold-the system proceeds to a second stage. Multiple agents engage in structured debate to synthesize conflicting evidence and generate well-reasoned verdicts with explicit justifications. Experimental results demonstrate that our two-stage approach achieves superior performance compared to baseline methods, highlighting the value of combining automated scoring with collaborative reasoning for complex verification tasks.

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