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Using NLP for Fact Checking: A Survey

Department of Engineering, Computing and Mathematical Sciences, Lewis University, Romeoville, IL 60446, USA
Author to whom correspondence should be addressed.
Academic Editors: Camelia Delcea, Liviu-Adrian Cotfas and Gabriella Ferruzzi
Designs 2021, 5(3), 42;
Received: 3 June 2021 / Revised: 1 July 2021 / Accepted: 6 July 2021 / Published: 14 July 2021
(This article belongs to the Special Issue Social Media Analysis)
In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use. View Full-Text
Keywords: natural language processing; machine learning; fact checking natural language processing; machine learning; fact checking
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MDPI and ACS Style

Lazarski, E.; Al-Khassaweneh, M.; Howard, C. Using NLP for Fact Checking: A Survey. Designs 2021, 5, 42.

AMA Style

Lazarski E, Al-Khassaweneh M, Howard C. Using NLP for Fact Checking: A Survey. Designs. 2021; 5(3):42.

Chicago/Turabian Style

Lazarski, Eric, Mahmood Al-Khassaweneh, and Cynthia Howard. 2021. "Using NLP for Fact Checking: A Survey" Designs 5, no. 3: 42.

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