University of Kwazulu-Natal

Pdf A Declarative Approach To Data Driven Fact Checking

Bonisiwe Shabane
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pdf a declarative approach to data driven fact checking

Fact checking is an essential part of any investigative work. For linguistic, psychological and social reasons, it is an inherently human task. Yet, modern media make it increasingly difficult for experts to keep up with the pace at which information is produced. Hence, we believe there is value in tools to assist them in this process. Much of the effort on Web data research has been focused on coping with incompleteness and uncertainty. Comparatively, dealing with context has received less attention, although it is crucial in judging the validity of a claim.

For instance, what holds true in a US state, might not in its neighbors, e.g., due to obsolete or superseded laws. In this work, we address the problem of checking the validity of claims in multiple contexts. We define a language to represent and query facts across different dimensions. The approach is non-intrusive and allows relatively easy modeling, while capturing incompleteness and uncertainty. We describe the syntax and semantics of the language. We present algorithms to demonstrate its feasibility, and we illustrate its usefulness through examples.

#1 A Declarative Approach to Data-Driven Fact Checking Github: https://github.com/bojone/papers.cool Please read our Disclaimer before proceeding. For more interesting features, please visit kexue.fm and kimi.ai. Artificial Intelligence Research Center, AIST No.

1: Thirty-First AAAI Conference On Artificial Intelligence Proceedings of the AAAI Conference on Artificial Intelligence, 31 Fact checking is an essential part of any investigative work. For linguistic, psychological and social reasons, it is an inherently human task. Yet, modern media make it increasingly difficult for experts to keep up with the pace at which information is produced. Hence, we believe there is value in tools to assist them in this process.

Much of the effort on Web data research has been focused on coping with incompleteness and uncertainty. Comparatively, dealing with context has received less attention, although it is crucial in judging the validity of a claim. For instance, what holds true in a US state, might not in its neighbors, e.g., due to obsolete or superseded laws. In this work, we address the problem of checking the validity of claims in multiple contexts. We define a language to represent and query facts across different dimensions. The approach is non-intrusive and allows relatively easy modeling, while capturing incompleteness and uncertainty.

We describe the syntax and semantics of the language. We present algorithms to demonstrate its feasibility, and we illustrate its usefulness through examples. Proceedings of the AAAI Conference on Artificial Intelligence, 31 Fact checking is an essential part of any investigative work. For linguistic, psychological and social reasons, it is an inherently human task. Yet, modern media make it increasingly difficult for experts to keep up with the pace at which information is produced.

Hence, we believe there is value in tools to assist them in this process. Much of the effort on Web data research has been focused on coping with incompleteness and uncertainty. Comparatively, dealing with context has received less attention, although it is crucial in judging the validity of a claim. For instance, what holds true in a US state, might not in its neighbors, e.g., due to obsolete or superseded laws. In this work, we address the problem of checking the validity of claims in multiple contexts. We define a language to represent and query facts across different dimensions.

The approach is non-intrusive and allows relatively easy modeling, while capturing incompleteness and uncertainty. We describe the syntax and semantics of the language. We present algorithms to demonstrate its feasibility, and we illustrate its usefulness through examples. Please note: Providing information about references and citations is only possible thanks to to the open metadata APIs provided by crossref.org and opencitations.net. If citation data of your publications is not openly available yet, then please consider asking your publisher to release your citation data to the public. For more information please see the Initiative for Open Citations (I4OC).

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You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q. This study examined four fact checkers (Snopes, PolitiFact, Logically, and the Australian Associated Press FactCheck) using a data-driven approach. First, we scraped 22,349 fact-checking articles from Snopes and PolitiFact and compared their results and agreement on verdicts. Generally, the two fact checkers agreed with each other, with only one conflicting verdict among 749 matching claims after adjusting minor rating differences.

Next, we assessed 1,820 fact-checking articles from Logically and the Australian Associated Press FactCheck and highlighted the differences in their fact-checking behaviors. Major events like the COVID-19 pandemic and the presidential election drove increased the frequency of fact-checking, with notable variations in ratings and authors across fact checkers. College of Information Sciences and Technology, The Pennsylvania State University, USA College of Information Sciences and Technology, The Pennsylvania State University, USA College of Information Sciences and Technology, The Pennsylvania State University, USA College of Information Sciences and Technology, The Pennsylvania State University, USA

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