Fake News Detection in Short Videos by Integrating Semantic Credibility and Multi-Granularity Contrastive Learning

Short videos have become a primary medium for news delivery, but their low cost, rapid diffusion, and multimodal nature make misinformation easier to generate and harder to verify. Existing methods often rely on single-modality cues or shallow cross-modal correlations, making it difficult to distinguish manipulations from benign edits and limiting interpretability. We propose a robust and interpretable framework for fake news detection in short videos. It combines LLM-based video understanding and online search for multi-dimensional credibility assessment, employs RoBERTa and capsule networks for semantic aggregation, and leverages a diffusion model with multi-granularity contrastive learning to enforce cross-modal consistency. A neuro-symbolic rule engine further calibrates predictions with logical constraints to provide traceable rationales. Experiments on the FakeSV dataset demonstrate an accuracy of 89.11% and an F1 score of 89.53%, significantly outperforming mainstream baseline models. This performance surpasses the current state-of-the-art OpEvFake model, which recorded an accuracy of 87.80% and an F1 score of 87.71%, and also substantially outperforms the representative short-video detection method SV-FEND, which achieved an accuracy of 81.69% and an F1 score of 81.78%. The framework shows robustness against emotional manipulation, title–content inconsistency, audio–video desynchronization, and local tampering, while offering explanatory evidence through rule triggers and modality contributions.