  Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned
image. Multimodal misinformation is perceived as more credible by humans, and
spreads faster than its text-only counterparts. While an increasing body of
research investigates automated fact-checking (AFC), previous surveys mostly
focus on text. In this survey, we conceptualise a framework for AFC including
subtasks unique to multimodal misinformation. Furthermore, we discuss related
terms used in different communities and map them to 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
