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aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions

Altuncu, Enes, Nurse, Jason R. C., Bagriacik, Meryem, Kaleba, Sophie, Yuan, Haiyue, Bonheme, Lisa, Li, Shujun (2023) aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions. In: Workshop Proceedings of the 17th International AAAI Conference on Web and Social Media. . 27:1-27:10. Association for the Advancement of Artificial Intelligence (doi:10.36190/2023.27) (KAR id:101790)

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In this highly digitised world, fake news is a challenging problem that can cause serious harm to society. Considering how fast fake news can spread, automated methods, tools and services for assisting users to do fact-checking (i.e., fake news detection) become necessary and helpful, for both professionals, such as journalists and researchers, and the general public such as news readers. Experts, especially researchers, play an essential role in informing people about truth and facts, which makes them a good proxy for non-experts to detect fake news by checking relevant expert opinions and comments. Therefore, in this paper, we present aedFaCT, a web browser extension that can help professionals and news readers perform fact-checking via the automatic discovery of expert opinions relevant to the news of concern via shared keywords. Our initial evaluation with three independent testers (who did not participate in the development of the extension) indicated that aedFaCT can provide a faster experience to its users compared with traditional fact-checking practices based on manual online searches, without degrading the quality of retrieved evidence for fact-checking. The source code of aedFaCT is publicly available at

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.36190/2023.27
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5105 Data transmission systems > TK5105.5 Computer networks > TK5105.875.I57 Internet
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
University-wide institutes > Institute of Cyber Security for Society
Funders: University of Kent (
Depositing User: Shujun Li
Date Deposited: 21 Jun 2023 13:52 UTC
Last Modified: 10 Oct 2023 11:56 UTC
Resource URI: (The current URI for this page, for reference purposes)
Altuncu, Enes:
Nurse, Jason R. C.:
Kaleba, Sophie:
Yuan, Haiyue:
Li, Shujun:
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