Affiliation:
1. Department of Computer Science and Technology, University of Cambridge, UK. zg283@cam.ac.uk
2. Department of Computer Science and Technology, University of Cambridge, UK. mss84@cam.ac.uk
3. Department of Computer Science and Technology, University of Cambridge, UK. av308@cam.ac.uk
Abstract
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 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.
Subject
Artificial Intelligence,Computer Science Applications,Linguistics and Language,Human-Computer Interaction,Communication
Reference205 articles.
1. Progress toward “the holy
grail”: The continued quest to automate
fact-checking;Adair,2017
2. Explainable fact checking with probabilistic
answer set programming;Ahmadi,2019
3. Simple open stance classification for
rumor analysis;Aker,2017
4. A survey on multimodal disinformation
detection;Alam;arXiv preprint arXiv:2103.12541,2021
5. Where is your evidence: Improving
fact-checking by justification modeling;Alhindi,2018
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