Automatic Fact CheckingThrough Multimodal Knowledge Bases

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Social media platforms are currently a primary source of information and opinion-formingcontent. However, due to their intrinsic nature of promoting engagement between its users,these platforms are rife with controversial, sensationalist, and often intentionally false rumors that can potentially cause economic, political, and social harms. The task of automatically debunking false rumors has significantly improved over the years, with many works proposing different approaches to solve the problem. Nevertheless, most of the rumor detection system relies solely on textual or on image/video data separately, without exploring the multimodality aspect of rumors. One approach that has proven value in related tasks is the use of knowledge bases. Therefore, this project proposes learning and applying a multimodal knowledge base to debunk false rumors or support true and verified information.