Fake news detection by Machine Learning in Latin America: A Systematic Review

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dc.contributor.author Nguema Ngomo, Jean Gabriel
dc.contributor.author Torres De Paiva, Raquel
dc.contributor.author Garcia, Ana Cristina
dc.date.accessioned 2023-12-26T18:38:42Z
dc.date.available 2023-12-26T18:38:42Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other adeba164-c8bb-464a-abd1-9d9d01b318f1
dc.identifier.uri https://hdl.handle.net/10125/106688
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining, and Machine Learning for Social Media
dc.subject fake news
dc.subject disinformation
dc.subject misinformation
dc.subject machine learning
dc.subject detection
dc.subject latin america
dc.subject social network
dc.subject social midia
dc.title Fake news detection by Machine Learning in Latin America: A Systematic Review
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract The growing spread of fake news on social media is a major scourge in society. To combat this problem, many studies have focused on different aspects to automatically detect fake news on social networks using artificial intelligence, especially Machine Learning. In the present work, to understand the current state of existing proposals, we conducted a systematic literature review. We propose to organize this literature in the light of a taxonomy of approaches.
dcterms.extent 10 pages
prism.startingpage 2526
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