Supervised classification methods for fake news identification

Truong, Thanh Cong; Diep, Quoc Bao; Zelinka, Ivan; Šenkeřík, Roman

dc.title	Supervised classification methods for fake news identification	en
dc.contributor.author	Truong, Thanh Cong
dc.contributor.author	Diep, Quoc Bao
dc.contributor.author	Zelinka, Ivan
dc.contributor.author	Šenkeřík, Roman
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.identifier.issn	0302-9743 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn	978-3-03-061533-8
dc.date.issued	2020
utb.relation.volume	12416 LNAI
dc.citation.spage	445
dc.citation.epage	454
dc.event.title	19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020
dc.event.location	Zakopane
utb.event.state-en	Poland
utb.event.state-cs	Polsko
dc.event.sdate	2020-10-12
dc.event.edate	2020-10-14
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	Springer Science and Business Media Deutschland GmbH
dc.identifier.doi	10.1007/978-3-030-61534-5_40
dc.relation.uri	https://link.springer.com/chapter/10.1007/978-3-030-61534-5_40
dc.subject	deep learning	en
dc.subject	fake news	en
dc.subject	machine learning	en
dc.subject	supervised classification	en
dc.description.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.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1010051
utb.identifier.obdid	43882340
utb.identifier.scopus	2-s2.0-85096571526
utb.source	d-scopus
dc.date.accessioned	2020-12-09T01:52:46Z
dc.date.available	2020-12-09T01:52:46Z
utb.contributor.internalauthor	Šenkeřík, Roman
utb.fulltext.affiliation	Thanh Cong Truong1, Quoc Bao Diep 1, Ivan Zelinka 1, Roman Senkerik 2 1 Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Ostrava, Czech Republic {cong.thanh.truong.st,ivan.zelinka}@vsb.cz, diepquocbao@gmail.com 2 Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01 Zlin, Czech Republic senkerik@utb.cz
utb.fulltext.dates	-
utb.fulltext.sponsorship	The following grants are acknowledged for the financial support provided for this research: Grant of SGS No. SP2020/78, VSB Technical University of Ostrava.
utb.scopus.affiliation	Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba, Ostrava, 708 00, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, 760 01, Czech Republic
utb.fulltext.projects	SP2020/78
utb.fulltext.faculty	Faculty of Applied Informatics