Cross-modal Attention Network with Orthogonal Latent Memory for Rumor Detection

Zekai Wu, Jiaxin Chen, Zhenguo Yang, Haoran Xie, Fu Lee Wang, Wenyin Liu

Abstract: In this paper, we design a cross-modal attention fusion network with orthogonal latent memory (CALM) to fuse multi-modal social media data for rumor detection. Given multimodal content features extracted from text and images, we devise a cross-modal attention fusion (CAF) mechanism to extract critical information underlying the modalities by intra-modality attention, and model the underlying relations among the modalities by inter-modality attention. In terms of the text, the natural sequential characteristics are critical to semantic understanding, while existing sequence models suffer from losing the information conveyed by the former words. To this end, we propose a Bi-GRU with orthogonal latent memory to extract the sequential features from the text, where the memory captures independent patterns. The fused content features and the sequential features can be used for rumor detection seamlessly. Extensive experiments conducted on two real-world datasets show the outperformance of the proposed CALM. (e.g., \(F_{1}\)-score is improved from 0.823 to 0.846 on Weibo dataset).