Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection

The development of social media enables fake news to be expressed in a multi-modal form, which is disseminated on various social platforms and brings harmful social impacts. To handle this challenge, the fake news detection task was proposed to examine whether false information is contained in multi-modal news. Existing methods exploit various approaches with cross-modal interaction and fusion, which have proven to be effective in detecting common fake news. However, although the description of multi-modal news is narrated around entities, the previously developed methods pay less attention to this characteristic. They do not explore its benefits to the detection task and underperform with respect to the detection of fake news that requires entity-centric comparisons. To make up for this omission, we explore a novel paradigm to detect fake news by aligning and fusing multi-modal entities and propose the Entity-oriented Multi-modal Alignment and Fusion network (EMAF). Our work adopts entity-centric cross-modal interaction, which can reserve semantic integrity and capture the details of multi-modal entities. Specifically, we design an Alignment module with the improved dynamic routing algorithm and introduce a Fusion module based on the comparison, the former aligns and captures the important entities and the latter compares and aggregates entity-centric features. Comparative experiments conducted on multiple public datasets, including Weibo, Twitter, and Reddit, reveal the superiority of the proposed EMAF method, and extensive analytical experiments demonstrate the effectiveness of our proposed modules.