Detecting and Explaining Fake News Short Videos with Multimodal Content and Real-World Evidence

2026-09-11

AI Summary

研究解决短视频平台假新闻检测与解释问题，提出NVKE-CEI系统，结合多模态内容和证据信息，通过关键帧提取和基于大语言模型的事实核查方法。

Abstract

Short-video platforms have become a primary news source for the public, which has also enabled the widespread dissemination of fake news videos. We study the task of fake news video detection and explanation (FNVDE). Existing methods face two critical limitations. First, commonly used frame selection strategies may omit veracity-relevant cues or provide insufficient temporal context for understanding news videos. Second, prior methods neglect either multimodal understanding or evidence retrieval. To address these limitations, we propose NVKE-CEI, a unified system that integrates a news video keyframes extraction method (NVKE) and an FNVDE framework leveraging both content and evidence information (CEI). NVKE selects keyframes based on chronological changes in combined visual and OCR-text similarity. CEI employs two specialized LLM-based fact checkers (content-based and evidence-based) whose outputs are fused by a lightweight judge model. Extensive experiments show that NVKE-CEI outperforms state-of-the-art baselines while generating high-quality content-grounded explanations.

Problem

• fake news
• short videos
• multimodal content
• evidence retrieval
• frame selection

Innovation

• multimodal understanding
• evidence retrieval
• keyframes extraction