FEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS

This paper discusses the necessity of fake news detection and how selected features can show differences between trustworthy and fake news. To demonstrate this concept, we first identified a set of features, that we believe can distinguish between fake and trustworthy news. We used these features...

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Autores principales:	Daniel CHOVANEC, Ján PARALIČ
Formato:	article
Lenguaje:	EN
Publicado:	Technical University of Kosice 2021
Materias:	analysis fake news fake news detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a6cf9dd362a04f6097410393ac996561
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id	oai:doaj.org-article:a6cf9dd362a04f6097410393ac996561
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spelling	oai:doaj.org-article:a6cf9dd362a04f6097410393ac9965612021-11-15T13:50:39ZFEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS10.15546/aeei-2021-00071335-82431338-3957https://doaj.org/article/a6cf9dd362a04f6097410393ac9965612021-11-01T00:00:00Zhttp://www.aei.tuke.sk/papers/2021/2/01_Chovanec.pdfhttps://doaj.org/toc/1335-8243https://doaj.org/toc/1338-3957This paper discusses the necessity of fake news detection and how selected features can show differences between trustworthy and fake news. To demonstrate this concept, we first identified a set of features, that we believe can distinguish between fake and trustworthy news. We used these features to analyse two real datasets and evaluated our results in various ways. We first used visual analysis by means of boxplots and evidenced the significance of differences by means of the Wilcox singed-rank test. As next, we used three different classification algorithms to train models for distinguishing between trustworthy and fake news using all important features. Finally, we used Principal Component Analysis (PCA) to visualize relations between identified features.Daniel CHOVANECJán PARALIČTechnical University of Kosice articleanalysisfake newsfake news detectionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENActa Electrotechnica et Informatica, Vol 21, Iss 2, Pp 3-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	analysis fake news fake news detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	analysis fake news fake news detection Electrical engineering. Electronics. Nuclear engineering TK1-9971 Daniel CHOVANEC Ján PARALIČ FEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS
description	This paper discusses the necessity of fake news detection and how selected features can show differences between trustworthy and fake news. To demonstrate this concept, we first identified a set of features, that we believe can distinguish between fake and trustworthy news. We used these features to analyse two real datasets and evaluated our results in various ways. We first used visual analysis by means of boxplots and evidenced the significance of differences by means of the Wilcox singed-rank test. As next, we used three different classification algorithms to train models for distinguishing between trustworthy and fake news using all important features. Finally, we used Principal Component Analysis (PCA) to visualize relations between identified features.
format	article
author	Daniel CHOVANEC Ján PARALIČ
author_facet	Daniel CHOVANEC Ján PARALIČ
author_sort	Daniel CHOVANEC
title	FEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS
title_short	FEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS
title_full	FEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS
title_fullStr	FEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS
title_full_unstemmed	FEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS
title_sort	features to distinguish between trustworthy and fake news
publisher	Technical University of Kosice
publishDate	2021
url	https://doaj.org/article/a6cf9dd362a04f6097410393ac996561
work_keys_str_mv	AT danielchovanec featurestodistinguishbetweentrustworthyandfakenews AT janparalic featurestodistinguishbetweentrustworthyandfakenews
_version_	1718428451982016512

FEATURES TO DISTINGUISH BETWEEN TRUSTWORTHY AND FAKE NEWS

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