A Survey on Automated Fact-Checking

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 lear...

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Bibliographic Details
Published inTransactions of the Association for Computational Linguistics Vol. 10; pp. 178 - 206
Main Authors Guo, Zhijiang, Schlichtkrull, Michael, Vlachos, Andreas
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 09.02.2022
MIT Press Journals, The
The MIT Press
Subjects
Artificial intelligence
Automation
COVID-19
Datasets
False information
Knowledge representation
Language
Linguistics
Machine learning
Natural language processing
Polls & surveys
Social networks
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Summary: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.
Bibliography:2022
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ISSN:2307-387X
2307-387X
DOI:10.1162/tacl_a_00454