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...

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Vydáno v:	Arabian journal for science and engineering Ročník 47; číslo 8; s. 10453 - 10469
Hlavní autoři:	Himdi, Hanen, Weir, George, Assiri, Fatmah, Al-Barhamtoshy, Hassanin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022 Springer Nature B.V
Témata:	Arabic language Computer Engineering and Computer Science Engineering Feature extraction Human performance Humanities and Social Sciences Machine learning multidisciplinary Natural language processing News Research Article-Computer Engineering and Computer Science Science Text analysis Natural language processing Deceptive text Fake news Machine learning
ISSN:	2193-567X, 1319-8025, 2191-4281
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Abstract	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.
AbstractList	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. 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.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.
Author	Al-Barhamtoshy, Hassanin Himdi, Hanen Weir, George Assiri, Fatmah
Author_xml	– sequence: 1 givenname: Hanen orcidid: 0000-0002-0182-2511 surname: Himdi fullname: Himdi, Hanen email: hanen.himdi@strath.ac.uk, hhimdi@uj.edu.sa organization: Department of Computer and Information Sciences, University of Strathclyde – sequence: 2 givenname: George surname: Weir fullname: Weir, George organization: Department of Computer and Information Sciences, University of Strathclyde – sequence: 3 givenname: Fatmah surname: Assiri fullname: Assiri, Fatmah organization: Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah – sequence: 4 givenname: Hassanin surname: Al-Barhamtoshy fullname: Al-Barhamtoshy, Hassanin organization: IT Department, Faculty of Computing and Information Technology, King Abdulaziz University
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Keywords	Natural language processing Deceptive text Fake news Machine learning
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SubjectTerms	Arabic language Computer Engineering and Computer Science Engineering Feature extraction Human performance Humanities and Social Sciences Machine learning multidisciplinary Natural language processing News Research Article-Computer Engineering and Computer Science Science Text analysis
Title	Arabic Fake News Detection Based on Textual Analysis
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