{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T02:08:20Z","timestamp":1780625300949,"version":"3.54.1"},"reference-count":205,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":41,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,2,9]]},"abstract":"Abstract<\/jats:title>\n Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research.<\/jats:p>","DOI":"10.1162\/tacl_a_00454","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T15:20:22Z","timestamp":1662477622000},"page":"178-206","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":325,"title":["A Survey on Automated Fact-Checking"],"prefix":"10.1162","volume":"10","author":[{"given":"Zhijiang","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, UK. zg283@cam.ac.uk"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Schlichtkrull","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, UK. mss84@cam.ac.uk"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Vlachos","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, UK. av308@cam.ac.uk"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"2022090615065376400_bib1","article-title":"Progress toward \u201cthe holy\n grail\u201d: The continued quest to automate\n fact-checking","volume-title":"Proceedings of the 2017\n Computation+Journalism Symposium","author":"Adair","year":"2017"},{"key":"2022090615065376400_bib2","doi-asserted-by":"publisher","DOI":"10.36370\/tto.2019.15","article-title":"Explainable fact checking with probabilistic\n answer set programming","volume-title":"Proceedings of the 2019\n Truth and Trust Online Conference (TTO 2019), London, UK, October\n 4\u20135, 2019","author":"Ahmadi","year":"2019"},{"key":"2022090615065376400_bib3","first-page":"31","article-title":"Simple open stance classification for\n rumor analysis","volume-title":"Proceedings of the International\n Conference Recent Advances in Natural Language Processing, RANLP\n 2017","author":"Aker","year":"2017"},{"key":"2022090615065376400_bib4","doi-asserted-by":"publisher","DOI":"10.26615\/978-954-452-049-6_005","article-title":"A survey on multimodal disinformation\n detection","author":"Alam","year":"2021","journal-title":"arXiv preprint arXiv:2103.12541"},{"key":"2022090615065376400_bib5","doi-asserted-by":"publisher","first-page":"85","DOI":"10.18653\/v1\/W18-5513","article-title":"Where is your evidence: Improving\n fact-checking by justification modeling","volume-title":"Proceedings of the First Workshop on Fact Extraction and\n VERification (FEVER)","author":"Alhindi","year":"2018"},{"key":"2022090615065376400_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.websem.2021.100663","article-title":"Time-aware evidence ranking for\n fact-checking","author":"Allein","year":"2021","journal-title":"Web\n Semantics"},{"key":"2022090615065376400_bib7","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2021.100663","article-title":"FEVEROUS: Fact Extraction and VERification\n over unstructured and structured information","author":"Aly","year":"2021","journal-title":"35th\n Conference on Neural Information Processing Systems (NeurIPS 2021) Track on\n Datasets and Benchmarks"},{"issue":"6","key":"2022090615065376400_bib8","doi-asserted-by":"publisher","first-page":"939","DOI":"10.14778\/3380750.3380762","article-title":"On detecting cherry-picked\n trendlines","volume":"13","author":"Asudeh","year":"2020","journal-title":"Proceedings of the VLDB\n Endowment"},{"key":"2022090615065376400_bib9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98932-7_32","article-title":"Overview of the CLEF-2018 CheckThat! 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