| dc.contributor | Universidade Estadual Paulista (UNESP) | |
| dc.date.accessioned | 2022-05-01T15:46:20Z | |
| dc.date.accessioned | 2022-12-20T03:51:11Z | |
| dc.date.available | 2022-05-01T15:46:20Z | |
| dc.date.available | 2022-12-20T03:51:11Z | |
| dc.date.created | 2022-05-01T15:46:20Z | |
| dc.date.issued | 2022-01-01 | |
| dc.identifier | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13208 LNAI, p. 57-67. | |
| dc.identifier | 1611-3349 | |
| dc.identifier | 0302-9743 | |
| dc.identifier | http://hdl.handle.net/11449/234317 | |
| dc.identifier | 10.1007/978-3-030-98305-5_6 | |
| dc.identifier | 2-s2.0-85127101959 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5414418 | |
| dc.description.abstract | Fake news has become a research topic of great importance in Natural Language Processing due to its negative impact on our society. Although its pertinence, there are few datasets available in Brazilian Portuguese and mostly comprise few samples. Therefore, this paper proposes creating a new fake news dataset named FakeRecogna that contains a greater number of samples, more up-to-date news, and covering a few of the most important categories. We perform a toy evaluation over the created dataset using traditional classifiers such as Naive Bayes, Optimum-Path Forest, and Support Vector Machines. A Convolutional Neural Network is also evaluated in the context of fake news detection in the proposed dataset. | |
| dc.language | eng | |
| dc.relation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
| dc.source | Scopus | |
| dc.subject | Corpus | |
| dc.subject | Fake news | |
| dc.subject | Portuguese | |
| dc.title | FakeRecogna: A New Brazilian Corpus for Fake News Detection | |
| dc.type | Actas de congresos | |
