Content-based fake news classification through modified voting ensemble

Credibility is a crucial element for journalism. As fake news impacts credibility, it affects the general public, policymakers, decision-makers, and the journalistic environment. However, current research on fake news using content-based approaches focuses majorly on one or two dimensions of stylome...

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Autor principal:	Jose Fabio Ribeiro Bezerra
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	fake news classification style analysis stylometrics content-based Telecommunication TK5101-6720 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/c8921557ed614dde8c1252129d73d11c
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id	oai:doaj.org-article:c8921557ed614dde8c1252129d73d11c
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spelling	oai:doaj.org-article:c8921557ed614dde8c1252129d73d11c2021-11-17T14:22:00ZContent-based fake news classification through modified voting ensemble2475-18392475-184710.1080/24751839.2021.1963912https://doaj.org/article/c8921557ed614dde8c1252129d73d11c2021-10-01T00:00:00Zhttp://dx.doi.org/10.1080/24751839.2021.1963912https://doaj.org/toc/2475-1839https://doaj.org/toc/2475-1847Credibility is a crucial element for journalism. As fake news impacts credibility, it affects the general public, policymakers, decision-makers, and the journalistic environment. However, current research on fake news using content-based approaches focuses majorly on one or two dimensions of stylometrics, semantic and linguistic processes, but not on these three simultaneously. Considering that content-based detection of fake news would benefit from a multidimensional approach because of their inherent characteristics, we proposed a method that uses all of these dimensions to improve classification accuracy, using a voting ensemble designed in an ensemble classifier form. The results show that the multidimensional voting classifier has produced more accurate results than its peers while being more sensitive to distinguish between true and false news when using randomized data.Jose Fabio Ribeiro BezerraTaylor & Francis Grouparticlefake newsclassificationstyle analysisstylometricscontent-basedTelecommunicationTK5101-6720Information technologyT58.5-58.64ENJournal of Information and Telecommunication, Vol 5, Iss 4, Pp 499-513 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fake news classification style analysis stylometrics content-based Telecommunication TK5101-6720 Information technology T58.5-58.64
spellingShingle	fake news classification style analysis stylometrics content-based Telecommunication TK5101-6720 Information technology T58.5-58.64 Jose Fabio Ribeiro Bezerra Content-based fake news classification through modified voting ensemble
description	Credibility is a crucial element for journalism. As fake news impacts credibility, it affects the general public, policymakers, decision-makers, and the journalistic environment. However, current research on fake news using content-based approaches focuses majorly on one or two dimensions of stylometrics, semantic and linguistic processes, but not on these three simultaneously. Considering that content-based detection of fake news would benefit from a multidimensional approach because of their inherent characteristics, we proposed a method that uses all of these dimensions to improve classification accuracy, using a voting ensemble designed in an ensemble classifier form. The results show that the multidimensional voting classifier has produced more accurate results than its peers while being more sensitive to distinguish between true and false news when using randomized data.
format	article
author	Jose Fabio Ribeiro Bezerra
author_facet	Jose Fabio Ribeiro Bezerra
author_sort	Jose Fabio Ribeiro Bezerra
title	Content-based fake news classification through modified voting ensemble
title_short	Content-based fake news classification through modified voting ensemble
title_full	Content-based fake news classification through modified voting ensemble
title_fullStr	Content-based fake news classification through modified voting ensemble
title_full_unstemmed	Content-based fake news classification through modified voting ensemble
title_sort	content-based fake news classification through modified voting ensemble
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/c8921557ed614dde8c1252129d73d11c
work_keys_str_mv	AT josefabioribeirobezerra contentbasedfakenewsclassificationthroughmodifiedvotingensemble
_version_	1718425445159927808

Content-based fake news classification through modified voting ensemble

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