Improving fake news classification using dependency grammar.

Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentence...

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Autores principales:	Kitti Nagy, Jozef Kapusta
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0f5222ec541842eca3ca282f2ed015f4
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id	oai:doaj.org-article:0f5222ec541842eca3ca282f2ed015f4
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spelling	oai:doaj.org-article:0f5222ec541842eca3ca282f2ed015f42021-12-02T20:14:38ZImproving fake news classification using dependency grammar.1932-620310.1371/journal.pone.0256940https://doaj.org/article/0f5222ec541842eca3ca282f2ed015f42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256940https://doaj.org/toc/1932-6203Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.Kitti NagyJozef KapustaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0256940 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kitti Nagy Jozef Kapusta Improving fake news classification using dependency grammar.
description	Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.
format	article
author	Kitti Nagy Jozef Kapusta
author_facet	Kitti Nagy Jozef Kapusta
author_sort	Kitti Nagy
title	Improving fake news classification using dependency grammar.
title_short	Improving fake news classification using dependency grammar.
title_full	Improving fake news classification using dependency grammar.
title_fullStr	Improving fake news classification using dependency grammar.
title_full_unstemmed	Improving fake news classification using dependency grammar.
title_sort	improving fake news classification using dependency grammar.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0f5222ec541842eca3ca282f2ed015f4
work_keys_str_mv	AT kittinagy improvingfakenewsclassificationusingdependencygrammar AT jozefkapusta improvingfakenewsclassificationusingdependencygrammar
_version_	1718374628953423872

Improving fake news classification using dependency grammar.

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