Multimodal Automated Fact-Checking: A Survey
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Misinformation, i.e. factually incorrect information, is often conveyed in multiple modalities, e.g. an image accompanied by a caption. It is perceived as more credible by humans, and spreads faster and wider than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on textual misinformation. In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation. Furthermore, we discuss related terminological developed in different communities in the context of our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and discuss limitations and promising directions for future research.
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