Multimodal Detection of Information Disorder from Social Media
This addresses the issue of information disorder on social media for users and platforms, but it is incremental as it builds on existing multimodal detection methods.
The paper tackled the problem of multimodal fake news detection on social media by proposing a network architecture that fuses textual, visual, user comments, and metadata at multiple levels, showing that multimodal analysis is highly effective and all modalities contribute positively when fused properly.
Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the problem of multimodal fake news detection on a largescale multimodal dataset. We propose a multimodal network architecture that enables different levels and types of information fusion. In addition to the textual and visual content of a posting, we further leverage secondary information, i.e. user comments and metadata. We fuse information at multiple levels to account for the specific intrinsic structure of the modalities. Our results show that multimodal analysis is highly effective for the task and all modalities contribute positively when fused properly.