Tracing truth: dynamic temporal networks for multi-modal fake news detection

As the internet continues to evolve rapidly and social media becomes increasingly prevalent, the ways people access information has become increasingly diverse. However, the proliferation of fake news has emerged as a critical problem, presenting major challenges to the integrity of the information ecosystem. To address the complex propagation mechanisms of fake news, existing studies leverage multi-modal information and dynamic propagation social graphs for effective detection. Nonetheless, capturing the temporal relationships of propagation nodes in dynamic social networks accurately and dynamically integrating multi-modal information for improved detection accuracy remains a technical challenge. In response, This study proposes a multimodal approach to fake news detection-the dynamic temporal network (DTN) model. Firstly, this model designs a time similarity strength metric to measure the temporal similarity among nodes in propagation sequences and introduces a weighting mechanism to dynamically fuse multi-modal information. Secondly, it constructs a social propagation graph model, enhancing node representation through the dynamic variations of time similarity and graph structure, and utilizes the Transformer encoder to extract the overall semantic features of news propagation. Furthermore, the model views the news propagation process as a complex system, analyzing the temporal dynamics of news in real social networks, effectively revealing the abnormal propagation patterns of fake news. Further analysis demonstrates that the proposed DTN model exhibits high accuracy and effectiveness in multi-modal fake news detection.