Arabic fake news detection based on textual analysis

Hanen Himdi, George Weir, Fatmah Assiri, Hassanin Al-Barhamtoshy

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
32 Downloads (Pure)


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 neg ative 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.
Original languageEnglish
Pages (from-to)10453-10469
Number of pages17
JournalArabian Journal for Science and Engineering
Issue number8
Early online date11 Feb 2022
Publication statusPublished - 31 Aug 2022


  • natural language processing
  • machine learning
  • deceptive text
  • fake news


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