Introduction: The rapid spread of false pandemic-related news on social media poses a serious threat to public health. Existing detection methods face challenges, such as unreliable edge connections and static network assumptions. This study aimed to develop a robust and adaptive framework for detecting fake pandemic news by addressing these limitations through dynamic interaction modeling and local feature analysis. Methods: We proposed a dynamic multi-scale hypergraph neural network framework. It employs a hypergraph neural network to capture complex interactions among user groups across time and a cross-time fusion mechanism to integrate temporal dynamics. Local user behavior features are extracted using propagation tree forests. Results: Experiments on multiple datasets showed that the proposed framework significantly outperformed existing propagation-based methods, achieving higher accuracy and F1 scores. Discussion: Although MSD-HNN achieved promising results, its reliance on textual features and the limited scale of certain datasets may affect generalizability. Future work will focus on incorporating multimodal data, enhancing model interpretability, and improving computational efficiency for realtime deployment. Conclusion: The dynamic multi-scale hypergraph neural network framework effectively detects fake pandemic news by leveraging global and local features. Future work will explore incorporating multimodal data to improve model generalization and adaptability.
Gao et al. (Mon,) studied this question.