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An intelligent cybersecurity system for detecting fake news in social media websites

People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on...

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Detalles Bibliográficos
Autores principales: Mughaid, Ala, Al-Zu’bi, Shadi, AL Arjan, Ahmed, AL-Amrat, Rula, Alajmi, Rathaa, Zitar, Raed Abu, Abualigah, Laith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021563/
https://www.ncbi.nlm.nih.gov/pubmed/35469124
http://dx.doi.org/10.1007/s00500-022-07080-1
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author Mughaid, Ala
Al-Zu’bi, Shadi
AL Arjan, Ahmed
AL-Amrat, Rula
Alajmi, Rathaa
Zitar, Raed Abu
Abualigah, Laith
author_facet Mughaid, Ala
Al-Zu’bi, Shadi
AL Arjan, Ahmed
AL-Amrat, Rula
Alajmi, Rathaa
Zitar, Raed Abu
Abualigah, Laith
author_sort Mughaid, Ala
collection PubMed
description People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy.
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spelling pubmed-90215632022-04-21 An intelligent cybersecurity system for detecting fake news in social media websites Mughaid, Ala Al-Zu’bi, Shadi AL Arjan, Ahmed AL-Amrat, Rula Alajmi, Rathaa Zitar, Raed Abu Abualigah, Laith Soft comput Data Analytics and Machine Learning People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy. Springer Berlin Heidelberg 2022-04-21 2022 /pmc/articles/PMC9021563/ /pubmed/35469124 http://dx.doi.org/10.1007/s00500-022-07080-1 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Data Analytics and Machine Learning
Mughaid, Ala
Al-Zu’bi, Shadi
AL Arjan, Ahmed
AL-Amrat, Rula
Alajmi, Rathaa
Zitar, Raed Abu
Abualigah, Laith
An intelligent cybersecurity system for detecting fake news in social media websites
title An intelligent cybersecurity system for detecting fake news in social media websites
title_full An intelligent cybersecurity system for detecting fake news in social media websites
title_fullStr An intelligent cybersecurity system for detecting fake news in social media websites
title_full_unstemmed An intelligent cybersecurity system for detecting fake news in social media websites
title_short An intelligent cybersecurity system for detecting fake news in social media websites
title_sort intelligent cybersecurity system for detecting fake news in social media websites
topic Data Analytics and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021563/
https://www.ncbi.nlm.nih.gov/pubmed/35469124
http://dx.doi.org/10.1007/s00500-022-07080-1
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