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
@article{arxiv.2305.13507,
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},
journal= {arXiv preprint arXiv:2305.13507},
year = {2023}
}
Comments
The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP): Findings