Which machine learning paradigm for fake news detection?

dc.creator	Katsaros D., Stavropoulos G., Papakostas D.	en
dc.date.accessioned	2023-01-31T08:33:30Z
dc.date.available	2023-01-31T08:33:30Z
dc.date.issued	2019
dc.identifier	10.1145/3350546.3352552
dc.identifier.isbn	9781450369343
dc.identifier.uri	http://hdl.handle.net/11615/74617
dc.description.abstract	Fake news detection/classification is gradually becoming of paramount importance to out society in order to avoid the so-called reality vertigo, and protect in particular the less educated persons. Various machine learning techniques have been proposed to address this issue. This article presents a comprehensive performance evaluation of eight machine learning algorithms for fake news detection/classification. © 2019 Association for Computing Machinery.	en
dc.language.iso	en	en
dc.source	Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019	en
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074785254&doi=10.1145%2f3350546.3352552&partnerID=40&md5=72d03a44f9f4f866f48b86171ef0c965
dc.subject	Learning algorithms	en
dc.subject	Comprehensive performance evaluation	en
dc.subject	Machine learning techniques	en
dc.subject	Machine learning	en
dc.subject	Association for Computing Machinery, Inc	en
dc.title	Which machine learning paradigm for fake news detection?	en
dc.type	conferenceItem	en

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