Kontaktujte nás | Jazyk: čeština English
Název: | Supervised classification methods for fake news identification | ||||||||||
Autor: | Truong, Thanh Cong; Diep, Quoc Bao; Zelinka, Ivan; Šenkeřík, Roman | ||||||||||
Typ dokumentu: | Článek ve sborníku (English) | ||||||||||
Zdrojový dok.: | 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 | ||||||||||
Abstrakt: | 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. | ||||||||||
Plný text: | https://link.springer.com/chapter/10.1007/978-3-030-61534-5_40 | ||||||||||
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