From Detection to Understanding: Multi-Turn Reasoning for Video Misinformation Analysis
Zhi Zeng, Jiaying Wu National University of Singapore, Minnan Luo Xi'an Jiaotong University, Di Zhang, Yifei Yang, Xiangzheng Kong, Herun Wan, Zihan Ma
Video misinformation detection is often approached as a binary veracity classification problem, overlooking the complex reasoning required to explain how and why content misleads. Existing benchmarks fail to capture the diversity of manipulation strategies, such as AI-generated edits and out-of-context manipulation, and do not evaluate whether models can provide process-level justifications for their judgments. We address these limitations with MisVideoQA, a multi-turn benchmark designed to assess comprehensive understanding and reasoning in video misinformation analysis. MisVideoQA covers 12 fine-grained deception categories and evaluates models along six dimensions, progressing from perceptual attribution to intent and persuasion analysis. Recognizing that standard MLLMs struggle to sustain such structured, evidence-based deduction, we propose MisAgent, a Delphi-inspired multi-agent framework in which specialized agents collaboratively integrate multimodal cues with external evidence. Experimental results show that state-of-the-art multimodal large language models perform poorly on MisVideoQA, while MisAgent consistently improves reasoning accuracy and explanation quality. Together, our benchmark and framework establish a unified foundation for reliable, interpretable, and evidence-grounded video misinformation analysis.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表