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

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 o...

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Detalles Bibliográficos
Autores principales: Li, Xiaojun, Li, Shaochen, Li, Jia, Yao, Junping, Xiao, Xvhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352884/
https://www.ncbi.nlm.nih.gov/pubmed/34373531
http://dx.doi.org/10.1038/s41598-021-95514-5
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author Li, Xiaojun
Li, Shaochen
Li, Jia
Yao, Junping
Xiao, Xvhao
author_facet Li, Xiaojun
Li, Shaochen
Li, Jia
Yao, Junping
Xiao, Xvhao
author_sort Li, Xiaojun
collection PubMed
description 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%.
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spelling pubmed-83528842021-08-10 Detection of fake-video uploaders on social media using Naive Bayesian model with social cues Li, Xiaojun Li, Shaochen Li, Jia Yao, Junping Xiao, Xvhao Sci Rep Article 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%. Nature Publishing Group UK 2021-08-09 /pmc/articles/PMC8352884/ /pubmed/34373531 http://dx.doi.org/10.1038/s41598-021-95514-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Xiaojun
Li, Shaochen
Li, Jia
Yao, Junping
Xiao, Xvhao
Detection of fake-video uploaders on social media using Naive Bayesian model with social cues
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352884/
https://www.ncbi.nlm.nih.gov/pubmed/34373531
http://dx.doi.org/10.1038/s41598-021-95514-5
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