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

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    Nguyen437455-Accepted.pdf (1.167Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Nguyen, Thanh Tam
    Weidlich, Matthias
    Yin, Hongzhi
    Zheng, Bolong
    Nguyen, Quang Huy
    Nguyen, Quoc Viet Hung
    Griffith University Author(s)
    Nguyen, Henry
    Nguyen, Thanh Tam
    Year published
    2020
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    Abstract
    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. ...
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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. 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
    DOI
    https://doi.org/10.1145/3397271.3401408
    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
    Publication URI
    http://hdl.handle.net/10072/399264
    Collection
    • Conference outputs

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