Multimodal Automated Fact-Checking: A Survey

Mubashara Akhtar, Michael Schlichtkrull, Zhijiang Guo, Oana Cocarascu, Elena Simperl +1 more
5/22/2023
cs.CLcs.AIcs.CV

No official code found. Best implementation: Cartus/Automated-Fact-Checking-Resources (580 stars, MIT, last commit 2025-02-23).

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

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Code Implementations(1)

Cartus/Automated-Fact-Checking-ResourcesCommunity100%
MIT

Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).

58063Jul 9, 20211 years agoMIT
claim-detectiondatasetexplainable-aifact-checkingfake-news-detection+7 more

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Cite this paper

@article{akhtar2023multimodal,
  title  = {Multimodal Automated Fact-Checking: A Survey},
  author = {Mubashara Akhtar and Michael Schlichtkrull and Zhijiang Guo and Oana Cocarascu and Elena Simperl and Andreas Vlachos},
  year   = {2023},
  eprint = {2305.13507},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.13507v3}
}

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