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.
Li et al. (Fri,) studied this question.