Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence

Although multimodal feature fusion technology in fake news detection can integrate complementary information from different modal data, the semantic inconsistency of multimodal features will lead to feature fusion difficulties. And there is the problem of information loss during one fusion process. In addition, although it is possible to improve the detection effect by increasing the support of external evidence in fake news detection, there is a lag in obtaining external evidence and the reliability and completeness of the evidence source is difficult to guarantee. Additional noise may be introduced to interfere with the model judgment. Therefore, a cross-modal fake news detection method (CM-MLF) based on evidence-free multilevel fusion is proposed. The method solves the semantic inconsistency problem by utilizing cross-modal alignment processing. And it utilizes the attention mechanism to perform multilevel fusion of text and image features without the assistance of other evidential features to further enhance the expressive power of the features. Experiments show that the method achieves better detection results on multiple benchmark datasets, effectively improving the accuracy and robustness of cross-modal fake news detection.