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A Survey on Automated Fact-Checking

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2022.tacl-1.11.pdf

Summary

Manual fact-checking cannot keep pace with how quickly claims, true or false, spread online. Verifying a single statement can take professionals hours or even days. This TACL survey from the University of Cambridge maps NLP research on automating that work. Earlier overviews either focused on isolated subtasks or left out two steps: choosing which claims to examine and explaining verdicts. The authors therefore propose a single pipeline. It begins with claim detection, continues with evidence retrieval, and ends with claim verification, which is split into verdict prediction and justification production. Using this structure, they catalogue existing datasets and modelling strategies. They also relate fact-checking to neighbouring problems such as rumour, stance, fake news and propaganda detection. Finally, they flag open problems, including subjective check-worthiness, unreliable evidence sources, multimodal claims and a strong bias toward English. The paper reports no new experiments. Its contribution is a common vocabulary and taxonomy plus a public resource list. Together these give newcomers, including teams building non-English systems, a clear map of the field and its future directions.

Key points

  • Framework: claim detection (check-worthiness or checkability, e.g., Konstantinovskiy et al., 2021) → evidence retrieval (text, tables, knowledge bases, images, metadata) → claim verification (verdict prediction + justification production).
  • Datasets and benchmarks named: Liar (claims taken from fact-checking sites), FEVER (built for research on Wikipedia), FEVEROUS (structured and unstructured evidence), and Fake News Challenge and Emergent for stance detection.
  • Motivation: an earlier study (Lewis et al., 2008) found that under half of published articles had been verified. Claim-only models that ignore evidence fail on well-written or machine-generated misinformation.
  • Multilinguality: only a handful of non-English datasets exist, and none support cross-lingual setups. Gupta and Srikumar (2021) span 25 languages, yet they observed no performance gain from including training examples in additional languages.
  • Resources and future directions: a companion list is available at github.com/Cartus/Automated-Fact-Checking-Resources. The authors argue for proactive "prebunking," which outperforms post-hoc debunking, as a direction for NLP. The work was funded by the ERC grant AVeriTeC.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

230 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 227 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00454

@article{guo2022survey,
  title = {{A Survey on Automated Fact-Checking}},
  author = {Guo, Zhijiang and Schlichtkrull, Michael and Vlachos, Andreas},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2022},
  doi = {10.1162/tacl\_a\_00454},
  url = {https://doi.org/10.1162/tacl_a_00454}
}

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