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        Chapter Semantic analysis of web archive historical data: 1983 “Marche pour l’égalité et contre le racisme”

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        Author(s)
        Rendina, Davide
        Gebeil, Sophie
        Génois, Mathieu
        Bellot, Patrice
        Language
        English
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        Abstract
        Based on a corpus composed by data obtained from the web archive of the French National Audiovisual Institute, including web pages referencing the history of the 1983 March for Equality and Against Racism, we explored how the memory of a historical event is built through the recounting of web media and the possibilities afforded by computational text analysis methods for the study of large corpuses of historical data from the archived web. This chapter presents the methodology and results of Davide Rendina's master's thesis in computer sciences under the supervision of Sophie Gebeil, Mathieu Génois, and Patrice Bellot. The objective is to demonstrate how historians can utilize archived HTML pages to study the media coverage of historical subjects on the web.
        URI
        https://library.oapen.org/handle/20.500.12657/96570
        Keywords
        anti-racism; media web archive; memory studies; topic modeling; 1983
        DOI
        10.36253/979-12-215-0413-2.22
        ISBN
        9791221504132, 9791221504132
        Publisher
        Firenze University Press
        Publisher website
        https://www.fupress.com/
        Publication date and place
        Florence, 2024
        Series
        Proceedings e report, 138
        Classification
        Library, archive and information management
        Bibliographic and subject control
        Archiving, preservation and digitization
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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