Multimodal Rumor Detection with Causal Graph Attention Network

In the era of big data, the automatic detection and governance of rumors are of great significance in protecting the privacy and security of users. Many previous works have achieved good results in rumor detection through multimodal feature fusion, background knowledge expansion, etc. Causality in rumors is a potential textual feature representing rumors’ linguistic logic and causality semantics. The rumors’ causality often appears inconsistent with the truth, based on which we can improve the rumor detection model. However, previous work has neglected the use of the feature of causality between entities in rumor texts. To address these challenges, we propose a Causal Graph Attention Network (CGANet) for multimodal rumor detection, which constructs a causal graph for the rumor dataset to achieve better detection performance. CGANet extracts the node and causal relationship features of causal graphs through graph learning and enhances the causal semantics of multimodal features, thereby improving the detection ability of the model on false causal semantic content. Finally, we use a self-attention classification network to obtain the rumor detection results. We conducted experiments on two public benchmark datasets, Pheme and Weibo, and achieved an accuracy of 92.81% and 91.32%, respectively, better performance than the state-of-the-art methods. It proves the advantage of causal graphs in rumor detection tasks.