Multimodal Fusion with Co-Attention Networks for Fake News Detection

Abstract

Fake news with textual and visual contents has a better story-telling ability than text-only contents, and can be spread quickly with social media. People can be easily deceived by such fake news, and traditional expert identification is labor-intensive. Therefore, automatic detection of multimodal fake news has become a new hot-spot issue. A shortcoming of existing approaches is their inability to fuse multi-modality features effectively. They simply concatenate unimodal features without considering inter-modality relations. Inspired by the way people read news with image and text, we propose a novel Multimodal Co-Attention Networks (MCAN) to better fuse textual and visual features for fake news detection. Extensive experiments conducted on two real-world datasets demonstrate that MCAN can learn inter-dependencies among multimodal features and outperforms state-of-the-art methods.