Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil
| Ano de defesa: | 2022 |
|---|---|
| Autor(a) principal: | |
| Orientador(a): | |
| Banca de defesa: | |
| Tipo de documento: | Dissertação |
| Tipo de acesso: | Acesso aberto |
| Idioma: | por |
| Instituição de defesa: |
Universidade Federal do Rio Grande do Norte
Brasil UFRN PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO |
| Programa de Pós-Graduação: |
Não Informado pela instituição
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| Departamento: |
Não Informado pela instituição
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| País: |
Não Informado pela instituição
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| Palavras-chave em Português: | |
| Link de acesso: | https://repositorio.ufrn.br/handle/123456789/46791 |
Resumo: | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES |
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Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and BrazilFake NewsSemi-Supervised LearningCOVID-19Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESFake News has been a big problem for society for a long time. It has been magnified, reaching worldwide proportions, mainly with the growth of social networks and instant chat platforms where any user can quickly interact with news, either by sharing, through likes and retweets or presenting hers/his opinion on the topic. Since this is a very fast phenomenon, it became humanly impossible to manually identify and highlight any fake news. Therefore, the search for automatic solutions for fake news identification, mainly using machine learning models, has grown a lot in recent times, due to the variety of topics as well as the variety of fake news propagated. Most solutions focus on supervised learning models, however, in some datasets, there is an absence of labels for most of the instances. For this, the literature presents the use of semi-supervised learning algorithms which are able to learn from a few labeled data. Thus, this work will investigate the use of semi-supervised learning models for the detection of fake news, using as a case study the outbreak of the Sars-CoV-2 virus, the COVID-19 pandemic. Our results have shown that we have an interesting methodology which can be used to built a new social media dataset and automatic label the samples using semi-supervised learning models. We also have as an important contribution a new fake news dataset.Universidade Federal do Rio Grande do NorteBrasilUFRNPROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃOAbreu, Marjory Cristiany da Costahttp://lattes.cnpq.br/2234040548103596Oliveira, Laura Emmanuella Alves dos Santos Santana de05069886436http://lattes.cnpq.br/8996581733787436Cavalcante, Everton Ranielly de Sousahttp://lattes.cnpq.br/5065548216266121Souza Neto, Placido Antônio dehttp://lattes.cnpq.br/3641504724164977Nascimento, Tuany Mariah Lima do2022-04-05T00:00:23Z2022-04-05T00:00:23Z2022-01-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfNASCIMENTO, Tuany Mariah Lima do. Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil. 2022. 54f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2022.https://repositorio.ufrn.br/handle/123456789/46791info:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRN2022-05-02T16:02:17Zoai:repositorio.ufrn.br:123456789/46791Repositório InstitucionalPUBhttp://repositorio.ufrn.br/oai/repositorio@bczm.ufrn.bropendoar:2022-05-02T16:02:17Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
| dc.title.none.fl_str_mv |
Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil |
| title |
Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil |
| spellingShingle |
Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil Nascimento, Tuany Mariah Lima do Fake News Semi-Supervised Learning COVID-19 |
| title_short |
Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil |
| title_full |
Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil |
| title_fullStr |
Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil |
| title_full_unstemmed |
Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil |
| title_sort |
Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil |
| author |
Nascimento, Tuany Mariah Lima do |
| author_facet |
Nascimento, Tuany Mariah Lima do |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Abreu, Marjory Cristiany da Costa http://lattes.cnpq.br/2234040548103596 Oliveira, Laura Emmanuella Alves dos Santos Santana de 05069886436 http://lattes.cnpq.br/8996581733787436 Cavalcante, Everton Ranielly de Sousa http://lattes.cnpq.br/5065548216266121 Souza Neto, Placido Antônio de http://lattes.cnpq.br/3641504724164977 |
| dc.contributor.author.fl_str_mv |
Nascimento, Tuany Mariah Lima do |
| dc.subject.por.fl_str_mv |
Fake News Semi-Supervised Learning COVID-19 |
| topic |
Fake News Semi-Supervised Learning COVID-19 |
| description |
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-04-05T00:00:23Z 2022-04-05T00:00:23Z 2022-01-14 |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| format |
masterThesis |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
NASCIMENTO, Tuany Mariah Lima do. Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil. 2022. 54f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2022. https://repositorio.ufrn.br/handle/123456789/46791 |
| identifier_str_mv |
NASCIMENTO, Tuany Mariah Lima do. Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil. 2022. 54f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2022. |
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https://repositorio.ufrn.br/handle/123456789/46791 |
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por |
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por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Universidade Federal do Rio Grande do Norte Brasil UFRN PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO |
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Universidade Federal do Rio Grande do Norte Brasil UFRN PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO |
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reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
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