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
| dc.contributor.advisor | Abreu, Marjory Cristiany da Costa | |
| dc.contributor.advisor-co1 | Oliveira, Laura Emmanuella Alves dos Santos Santana de | |
| dc.contributor.advisor-co1ID | 05069886436 | pt_BR |
| dc.contributor.advisor-co1Lattes | http://lattes.cnpq.br/8996581733787436 | pt_BR |
| dc.contributor.advisorLattes | http://lattes.cnpq.br/2234040548103596 | pt_BR |
| dc.contributor.author | Nascimento, Tuany Mariah Lima do | |
| dc.contributor.referees1 | Cavalcante, Everton Ranielly de Sousa | |
| dc.contributor.referees1Lattes | http://lattes.cnpq.br/5065548216266121 | pt_BR |
| dc.contributor.referees2 | Souza Neto, Placido Antônio de | |
| dc.contributor.referees2Lattes | http://lattes.cnpq.br/3641504724164977 | pt_BR |
| dc.date.accessioned | 2022-04-05T00:00:23Z | |
| dc.date.available | 2022-04-05T00:00:23Z | |
| dc.date.issued | 2022-01-14 | |
| dc.description.resumo | Fake 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. | pt_BR |
| dc.description.sponsorship | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES | pt_BR |
| dc.identifier.citation | 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. | pt_BR |
| dc.identifier.uri | https://repositorio.ufrn.br/handle/123456789/46791 | |
| dc.language | pt_BR | pt_BR |
| dc.publisher | Universidade Federal do Rio Grande do Norte | pt_BR |
| dc.publisher.country | Brasil | pt_BR |
| dc.publisher.initials | UFRN | pt_BR |
| dc.publisher.program | PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.subject | Fake News | pt_BR |
| dc.subject | Semi-Supervised Learning | pt_BR |
| dc.subject | COVID-19 | pt_BR |
| dc.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 | pt_BR |
| dc.type | masterThesis | pt_BR |
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