Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning

• URL: http://arxiv.org/abs/2505.16836v3
• Date: Wed, 15 Oct 2025 15:50:39 GMT
• Title: Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
• Authors: Fanrui Zhang, Dian Li, Qiang Zhang, Jun Chen, Gang Liu, Junxiong Lin, Jiahong Yan, Jiawei Liu, Zheng-Jun Zha,
• Abstract summary: Existing methods often overfit to rigid templates and lack deep reasoning over deceptive content.<n>We introduce FakeVV, a large-scale benchmark comprising over 100,000 video-text pairs with fine-grained, interpretable annotations.<n>We also propose Fact-R1, a framework that integrates deep reasoning with collaborative rule-based reinforcement learning.
• Score: 54.56271651170667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large-scale, diverse datasets. Existing methods often overfit to rigid templates and lack deep reasoning over deceptive content. To address these challenges, we introduce FakeVV, a large-scale benchmark comprising over 100,000 video-text pairs with fine-grained, interpretable annotations. In addition, we further propose Fact-R1, a novel framework that integrates deep reasoning with collaborative rule-based reinforcement learning. Fact-R1 is trained through a three-stage process: (1) misinformation long-Chain-of-Thought (CoT) instruction tuning, (2) preference alignment via Direct Preference Optimization (DPO), and (3) Group Relative Policy Optimization (GRPO) using a novel verifiable reward function. This enables Fact-R1 to exhibit emergent reasoning behaviors comparable to those observed in advanced text-based reinforcement learning systems, but in the more complex multimodal misinformation setting. Our work establishes a new paradigm for misinformation detection, bridging large-scale video understanding, reasoning-guided alignment, and interpretable verification.

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