Fake News Detection Based on Cross-Semantic Multimodal Data Fusion

With the rapid development of social networks, the proliferation of fake news has become a pressing global issue. Such misleading content is often fabricated through the integration of multimodal data, including text and images, leading to detrimental effects on both society and the economy. Despite significant advancements in fake news detection methods in recent years, most existing approaches primarily emphasize global semantic features, often neglecting the critical role of local semantic features in the detection process. Furthermore, when integrating multimodal features, these methods struggle to effectively capture intermodal associations, resulting in suboptimal fusion performance. To address these challenges, we propose a novel fake news detection model (CSMDF) for cross-semantic multimodal data fusion. Initially, feature encoders are utilized to extract global semantic features from both the text and image modalities of the target news. Subsequently, we design a local semantic feature extraction module to capture the local semantic features within each modality by utilizing clustering algorithms, central loss functions, fully connected layers, and other methods. Meanwhile, a feature fusion module based on a bidirectional gated recurrent unit (BiGRU) is employed to integrate both global and local semantic features, generating cross-semantic representations for each modality. Finally, we implement a Co-Attention mechanism to facilitate multimodal feature fusion by integrating cross-semantic features, establishing intermodal associations, and capturing their interactive relationships. Experimental results on real datasets demonstrate that CSMDF consistently outperforms state-of-the-art methods, enhancing multimodal fake news detection.