@inproceedings{akhtar-etal-2023-multimodal,
    title = "Multimodal Automated Fact-Checking: A Survey",
    author = "Akhtar, Mubashara  and
      Schlichtkrull, Michael  and
      Guo, Zhijiang  and
      Cocarascu, Oana  and
      Simperl, Elena  and
      Vlachos, Andreas",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.findings-emnlp.361/",
    doi = "10.18653/v1/2023.findings-emnlp.361",
    pages = "5430--5448",
    abstract = "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"
}