Fake Content Detection in the Information Exponential Spreading Era

Detalhes bibliográficos
Autor(a) principal: Ferreira, Fernando Henrique Gregório Paulos
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/143465
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
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spelling Fake Content Detection in the Information Exponential Spreading EraData miningSentiment AnalysisMachine LearningFake ContentDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecent years brought an information access democratization, allowing people to access a huge amount of information and the ability to share it, in a way that it can easily reach millions of people in a very short time. This allows to have right and wrong uses of this capabilities, that in some cases can be used to spread malicious content to achieve some sort of goal. Several studies have been made regarding text mining and sentiment analysis, aiming to spot fake information and avoid misinformation spreading. The trustworthiness and veracity of the information that is accessible to people is getting increasingly important, and in some cases critical, and can be seen has a huge challenge for the current digital era. This problem might be addressed with the help of science and technology. One question that we can do to ourselves is: How do we guarantee that there is a correct use of information, and that people can trust in the veracity of it? Using mathematics and statistics, combined with machine learning classification and predictive algorithms, using the current computation power of information systems, can help minimize the problem, or at least spot the potential fake information. One suggests developing a research work that aims to reach a model for the prediction of a given text content is trustworthy. The results were promising reaching a predicting model with good performance.Henriques, Roberto André PereiraRUNFerreira, Fernando Henrique Gregório Paulos2022-09-05T13:42:12Z2022-07-062022-07-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/143465TID:203058283enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:21:47Zoai:run.unl.pt:10362/143465Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:50:56.993291Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Fake Content Detection in the Information Exponential Spreading Era
title Fake Content Detection in the Information Exponential Spreading Era
spellingShingle Fake Content Detection in the Information Exponential Spreading Era
Ferreira, Fernando Henrique Gregório Paulos
Data mining
Sentiment Analysis
Machine Learning
Fake Content
title_short Fake Content Detection in the Information Exponential Spreading Era
title_full Fake Content Detection in the Information Exponential Spreading Era
title_fullStr Fake Content Detection in the Information Exponential Spreading Era
title_full_unstemmed Fake Content Detection in the Information Exponential Spreading Era
title_sort Fake Content Detection in the Information Exponential Spreading Era
author Ferreira, Fernando Henrique Gregório Paulos
author_facet Ferreira, Fernando Henrique Gregório Paulos
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Ferreira, Fernando Henrique Gregório Paulos
dc.subject.por.fl_str_mv Data mining
Sentiment Analysis
Machine Learning
Fake Content
topic Data mining
Sentiment Analysis
Machine Learning
Fake Content
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
publishDate 2022
dc.date.none.fl_str_mv 2022-09-05T13:42:12Z
2022-07-06
2022-07-06T00:00:00Z
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 http://hdl.handle.net/10362/143465
TID:203058283
url http://hdl.handle.net/10362/143465
identifier_str_mv TID:203058283
dc.language.iso.fl_str_mv eng
language eng
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instacron:RCAAP
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