Supervised Classification Methods for Fake News Identification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10247264" target="_blank" >RIV/61989100:27240/20:10247264 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/20:10247264
Result on the web
<a href="https://link.springer.com/content/pdf/10.1007%2F978-3-030-61534-5_40.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007%2F978-3-030-61534-5_40.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-61534-5_40" target="_blank" >10.1007/978-3-030-61534-5_40</a>

Result language
angličtina
Original language name
Supervised Classification Methods for Fake News Identification
Original language description
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. (C) 2020, Springer Nature Switzerland AG.
Czech name
—
Czech description
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Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 12415
ISBN
978-3-030-61400-3
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
445-454
Publisher name
Springer
Place of publication
Cham
Event location
Zakopané
Event date
Oct 12, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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