FakeRecogna: A New Brazilian Corpus for Fake News Detection
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-98305-5_6 http://hdl.handle.net/11449/234317 |
Resumo: | 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. |
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Repositório Institucional da UNESP |
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FakeRecogna: A New Brazilian Corpus for Fake News DetectionCorpusFake newsPortugueseFake 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.School of Sciences São Paulo State UniversitySchool of Sciences São Paulo State UniversityUniversidade Estadual Paulista (UNESP)Garcia, Gabriel L. [UNESP]Afonso, Luis C. S. [UNESP]Papa, João P. [UNESP]2022-05-01T15:46:20Z2022-05-01T15:46:20Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject57-67http://dx.doi.org/10.1007/978-3-030-98305-5_6Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13208 LNAI, p. 57-67.1611-33490302-9743http://hdl.handle.net/11449/23431710.1007/978-3-030-98305-5_62-s2.0-85127101959Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/234317Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
FakeRecogna: A New Brazilian Corpus for Fake News Detection |
title |
FakeRecogna: A New Brazilian Corpus for Fake News Detection |
spellingShingle |
FakeRecogna: A New Brazilian Corpus for Fake News Detection Garcia, Gabriel L. [UNESP] Corpus Fake news Portuguese |
title_short |
FakeRecogna: A New Brazilian Corpus for Fake News Detection |
title_full |
FakeRecogna: A New Brazilian Corpus for Fake News Detection |
title_fullStr |
FakeRecogna: A New Brazilian Corpus for Fake News Detection |
title_full_unstemmed |
FakeRecogna: A New Brazilian Corpus for Fake News Detection |
title_sort |
FakeRecogna: A New Brazilian Corpus for Fake News Detection |
author |
Garcia, Gabriel L. [UNESP] |
author_facet |
Garcia, Gabriel L. [UNESP] Afonso, Luis C. S. [UNESP] Papa, João P. [UNESP] |
author_role |
author |
author2 |
Afonso, Luis C. S. [UNESP] Papa, João P. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Garcia, Gabriel L. [UNESP] Afonso, Luis C. S. [UNESP] Papa, João P. [UNESP] |
dc.subject.por.fl_str_mv |
Corpus Fake news Portuguese |
topic |
Corpus Fake news Portuguese |
description |
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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-01T15:46:20Z 2022-05-01T15:46:20Z 2022-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-98305-5_6 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13208 LNAI, p. 57-67. 1611-3349 0302-9743 http://hdl.handle.net/11449/234317 10.1007/978-3-030-98305-5_6 2-s2.0-85127101959 |
url |
http://dx.doi.org/10.1007/978-3-030-98305-5_6 http://hdl.handle.net/11449/234317 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13208 LNAI, p. 57-67. 1611-3349 0302-9743 10.1007/978-3-030-98305-5_6 2-s2.0-85127101959 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
57-67 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1797790270486478848 |