Understand, Refine and Summarize: Multi-View Knowledge Progressive Enhancement Learning for Fake News Video Detection

As short videos become a dominant medium for news dissemination, fake news videos pose increasing threats to public trust and information integrity. Existing methods primarily focus on learning multimodal representations to predict binary veracity labels, yet they overlook the use of external evidence, which is important for identifying more sophisticated fake news that subtly exploits psychological cues and cognitive biases. Moreover, these approaches do not provide fine-grained attribution labels, which are essential for interpretable misinformation governance. To address these limitations, we introduce EvidSV, the first comprehensive benchmark supporting evidence- and attribution-aware fake news video detection. Drawing inspiration from the human cognitive process of interpreting news-related content, we propose MUKE, a multi-view knowledge progressive enhancement learning framework. By jointly analyzing both the news content and supporting evidence, MUKE (1) facilitates the understanding of news semantics to (2) progressively refine shared domain knowledge, and (3) adaptively summarizes multi-view knowledge to assess news veracity. Extensive experiments demonstrate that MUKE consistently outperforms existing methods in both fake news detection and attribution, and generalizes effectively to previously unseen domains. Our code is available at https://github.com/zzeng1998/EvidSV.