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

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dc.contributor.authorNguema Ngomo, Jean Gabriel
dc.contributor.authorTorres De Paiva, Raquel
dc.contributor.authorGarcia, Ana Cristina
dc.date.accessioned2023-12-26T18:38:42Z
dc.date.available2023-12-26T18:38:42Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.306
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otheradeba164-c8bb-464a-abd1-9d9d01b318f1
dc.identifier.urihttps://hdl.handle.net/10125/106688
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining, and Machine Learning for Social Media
dc.subjectfake news
dc.subjectdisinformation
dc.subjectmisinformation
dc.subjectmachine learning
dc.subjectdetection
dc.subjectlatin america
dc.subjectsocial network
dc.subjectsocial midia
dc.titleFake news detection by Machine Learning in Latin America: A Systematic Review
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThe 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.extent10 pages
prism.startingpage2526

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