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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Shrnutí: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.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-021-06449-y