FactCatch: Incremental Pay-as-You-Go Fact Checking with Minimal User Effort

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Author(s)
Nguyen, Thanh Tam
Weidlich, Matthias
Yin, Hongzhi
Zheng, Bolong
Nguyen, Quang Huy
Nguyen, Quoc Viet Hung
Year published
2020
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The open nature of the Web enables users to produce and propagate any content without authentication, which has been exploited to spread thousands of unverified claims via millions of online documents. Maintenance of credible knowledge bases thus has to rely on fact checking that constructs a trusted set of facts through credibility assessment. Due to an inherent lack of ground truth information and language ambiguity, fact checking cannot be done in a purely automated manner without compromising accuracy. However, state-of-the-art fact checking services, rely mostly on human validation, which is costly, slow, and non-transparent. ...
View more >The open nature of the Web enables users to produce and propagate any content without authentication, which has been exploited to spread thousands of unverified claims via millions of online documents. Maintenance of credible knowledge bases thus has to rely on fact checking that constructs a trusted set of facts through credibility assessment. Due to an inherent lack of ground truth information and language ambiguity, fact checking cannot be done in a purely automated manner without compromising accuracy. However, state-of-the-art fact checking services, rely mostly on human validation, which is costly, slow, and non-transparent. This paper presents FactCatch, a human-in-the-loop system to guide users in fact checking that aims at minimisation of the invested effort. It supports incremental quality estimation, mistake mitigation, and pay-as-you-go instantiation of a high-quality fact database.
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View more >The open nature of the Web enables users to produce and propagate any content without authentication, which has been exploited to spread thousands of unverified claims via millions of online documents. Maintenance of credible knowledge bases thus has to rely on fact checking that constructs a trusted set of facts through credibility assessment. Due to an inherent lack of ground truth information and language ambiguity, fact checking cannot be done in a purely automated manner without compromising accuracy. However, state-of-the-art fact checking services, rely mostly on human validation, which is costly, slow, and non-transparent. This paper presents FactCatch, a human-in-the-loop system to guide users in fact checking that aims at minimisation of the invested effort. It supports incremental quality estimation, mistake mitigation, and pay-as-you-go instantiation of a high-quality fact database.
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Conference Title
SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
Copyright Statement
© ACM 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, ISBN: 978-1-4503-8016-4, https://doi.org/10.1145/3397271.3401408
Subject
Artificial intelligence