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Social media is accompanied by an increasing proportion of content thatprovides fake information or misleading content, known as information disorder.In this paper, we study the problem of multimodal fake news detection on alargescale multimodal dataset. We propose a multimodal network architecturethat enables different levels and types of information fusion. In addition tothe textual and visual content of a posting, we further leverage secondaryinformation, i.e. user comments and metadata. We fuse information at multiplelevels to account for the specific intrinsic structure of the modalities. Ourresults show that multimodal analysis is highly effective for the task and allmodalities contribute positively when fused properly.

Multimodal Detection of Information Disorder from Social Media