| 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 |
