Federal award
a

Towards End-to-End Computer-Assisted Fact-CheckingL
September 1, 2017 - August 31, 2022
University of Texas at Arlington
Innovative Data Intelligence Research Laboratory - ClaimBuster
III: Small: Collaborative Research: This award was for continued development of ClaimBuster, an “end-to-end system for computer-assisted fact-checking” to “monitor live discourses, social media, and news to catch factual claims, detect matches with a curated repository of fact-checks from professionals, and deliver the matches instantly to readers and viewers.” ClaimBuster is intended to “become the first-ever automated fact-checking system for use on a broad spectrum of factual claims... its use will be expanded to verify claims in various types of narratives, discourses, and documents such as sports news, legal documents, and financial reports to benefit a large base of potential users including consumers, publishers, corporate competitors, and legal professionals, among others.” They claim to directly benefit consumers by “improving information accuracy and transparency and help news organizations speed their fact-checking process and also ensure the accuracy of their own news stories.” Businesses can use ClaimBuster to “identify falsehoods in their competitors' and their own reports and press releases.” It also assists professionals such as lawyers in verifying documents. ClaimBuster will use database query, data mining, and natural language processing techniques to aid fact-checking by “investigating how to model factual claims and produce their internal representations by creating taxonomies of claim templates in different domains, categorize claims based on the taxonomies, and generate internal representations through semantic parsing of the claims' textual forms... such domain-specific modeling and internal representation of claims will enable novel methods and systematic, coherent solutions for other components of the system.” For algorithmic fact-checking, they will “devise novel methods for translating claims into structured queries, keyword queries, and natural language questions. Results of these queries over general and domain-specific databases and knowledge graphs will be compared with the answers embedded in the claims themselves, “to verify if the claims check out.” They also claim that by viewing claims as parameterized queries, they will “develop methods based on perturbation analysis to find counter-arguments to claims and to find "interesting" factlets from datasets, which will help ClaimBuster in identifying "cherry-picking" claims - claims that are correct but misleading.”
Commentary:
This NSF grant supported further development of the "ClaimBuster" automated fact-checking system, including Big Brother-style monitoring of "live discourses" to deliver instant corrections from "professionals."
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Towards End-to-End Computer-Assisted Fact-Checking

University of Texas at Arlington
Innovative Data Intelligence Research Laboratory - ClaimBuster
National Science Foundation
320780
September 1, 2017
August 31, 2022
United States of America