A Survey on Automated Fact-Checking
Published version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
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.
Description
Keywords
Journal Title
Conference Name
Journal ISSN
2307-387X
Volume Title
Publisher
Publisher DOI
Sponsorship
European Commission Horizon 2020 (H2020) ERC (965576)