Comment-Context Dual Collaborative Masked Transformer Network for Fake News Detection

Jinguang Wang, Shengsheng Qian, Jun Hu, Richang Hong

Abstract: The rapid proliferation of social media data has led to the widespread dissemination of multi-modal fake news, prompting researchers to develop novel detection methods. Most fake news detection approaches mine the rich context information, including the text and image content of news and associated comments. However, existing methods are often insufficient to filter out irrelevant contexts, such as noisy words, redundant image regions, and spam comments, which may introduce noise into the model. Particularly, these approaches struggle to handle comments, which often contain the most severe noise. In many cases, only a minuscule portion of comments is relevant to the news. To overcome these limitations, our research introduces a novel Comment-Context Dual Collaborative Masked Transformer Network ( $C^{2}DCMTN$ ). To handle the irrelevant contexts, we propose a Multi-modal Masked Transformer Network. This network extends the traditional Transformer with a mask mechanism capable of dynamically obscuring irrelevant multi-modal context information. To effectively deal with comments, which can suffer from more severe noise issues, we have designed a Comment-Context Encoder that focuses solely on the most crucial comments. Comprehensive experiments on two publicly available real-world datasets confirm that $C^{2}DCMTN$ outperforms state-of-the-art methods.