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Arabic Fake News Detection Based on Textual Analysis
Over the years, social media has had a considerable impact on the way we share information and send messages. With this comes the problem of the rapid distribution of fake news which can have negative impacts on both individuals and society. Given the potential negative influence, detecting unmonito...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831872/ https://www.ncbi.nlm.nih.gov/pubmed/35194540 http://dx.doi.org/10.1007/s13369-021-06449-y |
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author | Himdi, Hanen Weir, George Assiri, Fatmah Al-Barhamtoshy, Hassanin |
author_facet | Himdi, Hanen Weir, George Assiri, Fatmah Al-Barhamtoshy, Hassanin |
author_sort | Himdi, Hanen |
collection | PubMed |
description | Over the years, social media has had a considerable impact on the way we share information and send messages. With this comes the problem of the rapid distribution of fake news which can have negative impacts on both individuals and society. Given the potential negative influence, detecting unmonitored ‘fake news’ has become a critical issue in mainstream media. While there are recent studies that built machine learning models that detect fake news in several languages, lack of studies in detecting fake news in the Arabic language is scare. Hence, in this paper, we study the issue of fake news detection in the Arabic language based on textual analysis. In an attempt to address the challenges of authenticating news, we introduce a supervised machine learning model that classifies Arabic news articles based on their context’s credibility. We also introduce the first dataset of Arabic fake news articles composed through crowdsourcing. Subsequently, to extract textual features from the articles, we create a unique approach of forming Arabic lexical wordlists and design an Arabic Natural Language Processing tool to perform textual features extraction. The findings of this study promises great results and outperformed human performance in the same task. |
format | Online Article Text |
id | pubmed-8831872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-88318722022-02-18 Arabic Fake News Detection Based on Textual Analysis Himdi, Hanen Weir, George Assiri, Fatmah Al-Barhamtoshy, Hassanin Arab J Sci Eng Research Article-Computer Engineering and Computer Science Over the years, social media has had a considerable impact on the way we share information and send messages. With this comes the problem of the rapid distribution of fake news which can have negative impacts on both individuals and society. Given the potential negative influence, detecting unmonitored ‘fake news’ has become a critical issue in mainstream media. While there are recent studies that built machine learning models that detect fake news in several languages, lack of studies in detecting fake news in the Arabic language is scare. Hence, in this paper, we study the issue of fake news detection in the Arabic language based on textual analysis. In an attempt to address the challenges of authenticating news, we introduce a supervised machine learning model that classifies Arabic news articles based on their context’s credibility. We also introduce the first dataset of Arabic fake news articles composed through crowdsourcing. Subsequently, to extract textual features from the articles, we create a unique approach of forming Arabic lexical wordlists and design an Arabic Natural Language Processing tool to perform textual features extraction. The findings of this study promises great results and outperformed human performance in the same task. Springer Berlin Heidelberg 2022-02-11 2022 /pmc/articles/PMC8831872/ /pubmed/35194540 http://dx.doi.org/10.1007/s13369-021-06449-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Research Article-Computer Engineering and Computer Science Himdi, Hanen Weir, George Assiri, Fatmah Al-Barhamtoshy, Hassanin Arabic Fake News Detection Based on Textual Analysis |
title | Arabic Fake News Detection Based on Textual Analysis |
title_full | Arabic Fake News Detection Based on Textual Analysis |
title_fullStr | Arabic Fake News Detection Based on Textual Analysis |
title_full_unstemmed | Arabic Fake News Detection Based on Textual Analysis |
title_short | Arabic Fake News Detection Based on Textual Analysis |
title_sort | arabic fake news detection based on textual analysis |
topic | Research Article-Computer Engineering and Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831872/ https://www.ncbi.nlm.nih.gov/pubmed/35194540 http://dx.doi.org/10.1007/s13369-021-06449-y |
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