Detecting fake news videos using keyframes and evidence fusion
Detecting and Explaining Fake News Short Videos with Multimodal Content and Real-World Evidence
Computer Vision and Pattern RecognitionMultimedia
Summary
Short videos online can spread fake news quickly, and it can be hard to spot which videos are false. The authors developed a new way to pick important video frames by looking at changes in both pictures and text seen in the video. They then use two separate computer checkers, one that looks at the video content and another that finds real-world proof, to decide if the video is fake. Their system combines these checkers’ opinions to detect fake news videos better and explain why.
What this means in practice
- •For social media platforms: Automatically detect and explain fake news videos before they spread widely.$Commercial implications: Enables selling fake news filtering tools to platforms needing to moderate user video content at scale.
- •For fact checking organizations: Provide automated evidence-backed reports on suspicious short videos to assist manual verification.
Authors
Yifeng Luo, Yupeng Li, Ming Tang, Jianxiong Guo, Liang Lan
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.