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Supervised classification methods for fake news identification

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Title: Supervised classification methods for fake news identification
Author: Truong, Thanh Cong; Diep, Quoc Bao; Zelinka, Ivan; Šenkeřík, Roman
Document type: Conference paper (English)
Source document: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020, vol. 12416 LNAI, p. 445-454
ISSN: 0302-9743 (Sherpa/RoMEO, JCR)
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ISBN: 978-3-03-061533-8
DOI: https://doi.org/10.1007/978-3-030-61534-5_40
Abstract: Along with the rapid increase in the popularity of online media, the proliferation of fake news and its propagation is also rising. Fake news can propagate with an uncontrollable speed without verification and can cause severe damages. Various machine learning and deep learning approaches have been attempted to classify the real and the false news. In this research, the author group presents a comprehensive performance evaluation of eleven supervised algorithms on three datasets for fake news classification. © 2020, Springer Nature Switzerland AG.
Full text: https://link.springer.com/chapter/10.1007/978-3-030-61534-5_40
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Citace článku v konferenčním sborníku:
TRUONG, Thanh Cong, Quoc Bao DIEP, Ivan ZELINKA a Roman ŠENKEŘÍK. Supervised classification methods for fake news identification. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [online]. Zakopane: Springer Science and Business Media Deutschland GmbH, 2020, s. 445-454. [cit. 2024-11-25]. ISSN 0302-9743. Dostupné z: https://link.springer.com/chapter/10.1007/978-3-030-61534-5_40.

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