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ReVera Framework: A Framework for Fact Checking Traceability

Published: 05 November 2021 Publication History

Abstract

Professional fact-checking tends to be costly and scalability challenging. For this reason, a series of methods to automate this process has been emerging. Unfortunately, these methods have been created with monolithic architectures, not making use of pre-built parts, and are harder to be understood by others. This work aims to propose a framework, where the development of methods can be done in a modular manner, creating workflows based on key steps that can be linked together to generate the input classification. For this, a study of the literature was conducted to identify the main processing steps of the methods, seeking to develop a solution that would guarantee flexibility of execution and interoperability between the different processing components. With this, the automatic verification process was described in a limited set of steps that can be performed, creating independent components that implement each of them. The framework makes use of a proposed data traceability ontology to map all generated data during execution under a unified vocabulary, which facilitates communication between these independent components.

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Cited By

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  • (2025)Fact Checking AI Generated ContentArtificial Intelligence for Strategic Communication10.1007/978-981-96-2575-8_9(269-302)Online publication date: 18-Feb-2025
  • (2024)Tracking of Disinformation Sources: Examining Pages and URLsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339162611:5(6242-6253)Online publication date: Oct-2024

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  1. ReVera Framework: A Framework for Fact Checking Traceability

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    cover image ACM Conferences
    WebMedia '21: Proceedings of the Brazilian Symposium on Multimedia and the Web
    November 2021
    271 pages
    ISBN:9781450386098
    DOI:10.1145/3470482
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    • SBC: Brazilian Computer Society
    • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
    • CAPES: Brazilian Higher Education Funding Council

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    New York, NY, United States

    Publication History

    Published: 05 November 2021

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    Author Tags

    1. fact checking
    2. framework
    3. natural languagem processing
    4. ontology

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    WebMedia '21
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    WebMedia '21: Brazilian Symposium on Multimedia and the Web
    November 5 - 12, 2021
    Minas Gerais, Belo Horizonte, Brazil

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    WebMedia '21 Paper Acceptance Rate 24 of 75 submissions, 32%;
    Overall Acceptance Rate 270 of 873 submissions, 31%

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    Cited By

    View all
    • (2025)Fact Checking AI Generated ContentArtificial Intelligence for Strategic Communication10.1007/978-981-96-2575-8_9(269-302)Online publication date: 18-Feb-2025
    • (2024)Tracking of Disinformation Sources: Examining Pages and URLsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339162611:5(6242-6253)Online publication date: Oct-2024

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