Study of different machine learning approaches for identification of fake news articles

In this document multiple machine learning approaches, including Supervised, Semi-supervised and Unsupervised learning are explored with the objective of finding the best algorithm for the task of identifying fake news. The corpus used consists on pure text data extracted from news articles. TF-IDF...

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Detalhes bibliográficos
Autor: Zurita Nicolas, Josep Ricard
Formato: tesis de maestría
Fecha de publicación:2019
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/184331
Acesso em linha:https://hdl.handle.net/2117/184331
Access Level:acceso abierto
Palavra-chave:Fake news
Machine learning
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Àrees temàtiques de la UPC::Informàtica
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spelling Study of different machine learning approaches for identification of fake news articlesZurita Nicolas, Josep RicardFake newsMachine learningFake newsAprenentatge automàticAprenentatge automàtic -- AlgorismesÀrees temàtiques de la UPC::InformàticaIn this document multiple machine learning approaches, including Supervised, Semi-supervised and Unsupervised learning are explored with the objective of finding the best algorithm for the task of identifying fake news. The corpus used consists on pure text data extracted from news articles. TF-IDF and word2vec features are studied. Python is used for the implementationOutgoingUniversitat Politècnica de CatalunyaWunnik, Lucas Philippe van20192019-10-0820202020-04-22master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfhttps://hdl.handle.net/2117/184331reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1843312026-05-27T15:37:01Z
dc.title.none.fl_str_mv Study of different machine learning approaches for identification of fake news articles
title Study of different machine learning approaches for identification of fake news articles
spellingShingle Study of different machine learning approaches for identification of fake news articles
Zurita Nicolas, Josep Ricard
Fake news
Machine learning
Fake news
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Àrees temàtiques de la UPC::Informàtica
title_short Study of different machine learning approaches for identification of fake news articles
title_full Study of different machine learning approaches for identification of fake news articles
title_fullStr Study of different machine learning approaches for identification of fake news articles
title_full_unstemmed Study of different machine learning approaches for identification of fake news articles
title_sort Study of different machine learning approaches for identification of fake news articles
dc.creator.none.fl_str_mv Zurita Nicolas, Josep Ricard
author Zurita Nicolas, Josep Ricard
author_facet Zurita Nicolas, Josep Ricard
author_role author
dc.contributor.none.fl_str_mv Wunnik, Lucas Philippe van
dc.subject.none.fl_str_mv Fake news
Machine learning
Fake news
Aprenentatge automàtic
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Àrees temàtiques de la UPC::Informàtica
topic Fake news
Machine learning
Fake news
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Aprenentatge automàtic -- Algorismes
Àrees temàtiques de la UPC::Informàtica
description In this document multiple machine learning approaches, including Supervised, Semi-supervised and Unsupervised learning are explored with the objective of finding the best algorithm for the task of identifying fake news. The corpus used consists on pure text data extracted from news articles. TF-IDF and word2vec features are studied. Python is used for the implementation
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-10-08
2020
2020-04-22
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/184331
url https://hdl.handle.net/2117/184331
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,228081

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