Automatic Detection of Health-Related Rumors: A Dual-Graph Collaborative Reasoning Framework Based on Causal Logic and Knowledge Graph

With the widespread use of social media, the propagation of health-related rumors has become a significant public health threat. Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures, with only a few recent approaches attempting causal inference; however, these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors. In this study, we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts, holding significant potential for health rumor detection. To this end, we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs. CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency, thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims. In experiments conducted on a dataset comprising 8430 health rumors, CKDG achieved an accuracy of 91.28% and an F1 score of 90.38%, representing improvements of 5.11% and 3.29% over the best baseline, respectively. Our results indicate that the integrated use of causal discovery and domain-specific knowledge graphs offers significant advantages for health rumor detection systems. This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts. We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems, thereby improving the reliability of public health information on social media.