Fine-Grained Fact-Checking for Short Videos: A Multi-Agent Report Generation Framework

Abstract

With the rapid growth of short video platforms, the spread of fake news in short video format has become increasingly complex and deceptive. While existing text/image fact-checking methods fail to identify multiple fake claims within a single video, video fake news detection methods do not provide fine-grained evidence-based analysis. To address this gap, we propose a novel task: Fine-grained Fact-Checking Report Generation for Short Videos. Given a short video containing textual, visual, and audio modalities, the goal is to automatically generate a structured report that identifies specific fake claims and provides detailed analyses based on external evidence. We construct a benchmark dataset annotated by domain experts, along with fine-grained evaluation questions. Furthermore, we propose Video Multi-agent Fine-grained Fact-checking (VMFF), a training-free multi-agent framework that simulates the workflow of professional fact-checkers through three modules: (1) Video Understanding, (2) Task Decomposition, and (3) Retrieval, Reasoning, and Generation. Experimental results demonstrate its effectiveness in fine-grained fact-checking report generation.