A Survey on Automated Fact-Checking
View / Open Files
Publication Date
2022-02-09Journal Title
Transactions of the Association for Computational Linguistics
ISSN
2307-387X
Volume
10
Pages
178-206
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Guo, Z., Schlichtkrull, M., & Vlachos, A. (2022). A Survey on Automated Fact-Checking. Transactions of the Association for Computational Linguistics, 10 178-206. https://doi.org/10.1162/tacl_a_00454
Abstract
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 factchecking 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.
Sponsorship
European Commission Horizon 2020 (H2020) ERC (865958)
European Commission Horizon 2020 (H2020) ERC (965576)
Identifiers
External DOI: https://doi.org/10.1162/tacl_a_00454
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334679
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.