Detection of fake-video uploaders on social media using Naive Bayesian model with social cues

Abstract With the rapid development of the Internet, the wide circulation of disinformation has considerably disrupted the search and recognition of information. Despite intensive research devoted to fake text detection, studies on fake short videos that inundate the Internet are rare. Fake videos,...

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Autores principales:	Xiaojun Li, Shaochen Li, Jia Li, Junping Yao, Xvhao Xiao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c518db61c1a1403998f9fb158cd89471
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spelling	oai:doaj.org-article:c518db61c1a1403998f9fb158cd894712021-12-02T16:27:45ZDetection of fake-video uploaders on social media using Naive Bayesian model with social cues10.1038/s41598-021-95514-52045-2322https://doaj.org/article/c518db61c1a1403998f9fb158cd894712021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95514-5https://doaj.org/toc/2045-2322Abstract With the rapid development of the Internet, the wide circulation of disinformation has considerably disrupted the search and recognition of information. Despite intensive research devoted to fake text detection, studies on fake short videos that inundate the Internet are rare. Fake videos, because of their quick transmission and broad reach, can increase misunderstanding, impact decision-making, and lead to irrevocable losses. Therefore, it is important to detect fake videos that mislead users on the Internet. Since it is difficult to detect fake videos directly, we probed the detection of fake video uploaders in this study with a vision to provide a basis for the detection of fake videos. Specifically, a dataset consisting of 450 uploaders of videos on diabetes and traditional Chinese medicine was constructed, five features of the fake video uploaders were proposed, and a Naive Bayesian model was built. Through experiments, the optimal feature combination was identified, and the proposed model reached a maximum accuracy of 70.7%.Xiaojun LiShaochen LiJia LiJunping YaoXvhao XiaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaojun Li Shaochen Li Jia Li Junping Yao Xvhao Xiao Detection of fake-video uploaders on social media using Naive Bayesian model with social cues
description	Abstract With the rapid development of the Internet, the wide circulation of disinformation has considerably disrupted the search and recognition of information. Despite intensive research devoted to fake text detection, studies on fake short videos that inundate the Internet are rare. Fake videos, because of their quick transmission and broad reach, can increase misunderstanding, impact decision-making, and lead to irrevocable losses. Therefore, it is important to detect fake videos that mislead users on the Internet. Since it is difficult to detect fake videos directly, we probed the detection of fake video uploaders in this study with a vision to provide a basis for the detection of fake videos. Specifically, a dataset consisting of 450 uploaders of videos on diabetes and traditional Chinese medicine was constructed, five features of the fake video uploaders were proposed, and a Naive Bayesian model was built. Through experiments, the optimal feature combination was identified, and the proposed model reached a maximum accuracy of 70.7%.
format	article
author	Xiaojun Li Shaochen Li Jia Li Junping Yao Xvhao Xiao
author_facet	Xiaojun Li Shaochen Li Jia Li Junping Yao Xvhao Xiao
author_sort	Xiaojun Li
title	Detection of fake-video uploaders on social media using Naive Bayesian model with social cues
title_short	Detection of fake-video uploaders on social media using Naive Bayesian model with social cues
title_full	Detection of fake-video uploaders on social media using Naive Bayesian model with social cues
title_fullStr	Detection of fake-video uploaders on social media using Naive Bayesian model with social cues
title_full_unstemmed	Detection of fake-video uploaders on social media using Naive Bayesian model with social cues
title_sort	detection of fake-video uploaders on social media using naive bayesian model with social cues
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c518db61c1a1403998f9fb158cd89471
work_keys_str_mv	AT xiaojunli detectionoffakevideouploadersonsocialmediausingnaivebayesianmodelwithsocialcues AT shaochenli detectionoffakevideouploadersonsocialmediausingnaivebayesianmodelwithsocialcues AT jiali detectionoffakevideouploadersonsocialmediausingnaivebayesianmodelwithsocialcues AT junpingyao detectionoffakevideouploadersonsocialmediausingnaivebayesianmodelwithsocialcues AT xvhaoxiao detectionoffakevideouploadersonsocialmediausingnaivebayesianmodelwithsocialcues
_version_	1718384015986130944

Detection of fake-video uploaders on social media using Naive Bayesian model with social cues

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