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Arabic fake news detection based on deep contextualized embedding models.

Ali Bou Nassif1,2, Ashraf Elnagar3, Omar Elgendy1

  • 1Department of Computer Engineering, University of Sharjah, P.O. Box: 27272, Sharjah, UAE.

Neural Computing & Applications
|May 9, 2022
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Summary
This summary is machine-generated.

Researchers developed advanced AI models for Arabic fake news detection, achieving over 98% accuracy. This work addresses the scarcity of resources for combating misinformation in the Arabic language.

Keywords:
Arabic fake newsContextualized modelsDeep learningNatural language processing

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Social media platforms are increasingly primary news sources, facilitating the rapid spread of misinformation.
  • Fake news significantly impacts various societal sectors, including politics and finance.
  • Existing research on fake news detection predominantly focuses on English, leaving a gap in Arabic language resources.

Discussion:

  • This study introduces a novel, large-scale Arabic fake news dataset.
  • It evaluates transformer-based classifiers using eight state-of-the-art Arabic contextualized embedding models, many for the first time in this domain.
  • A comprehensive analysis compares these models' performance against existing fake news detection systems.

Key Insights:

  • Transformer-based models utilizing advanced Arabic contextualized embeddings demonstrate high robustness in fake news detection.
  • The developed classifiers achieved an accuracy exceeding 98% on the constructed dataset.
  • The findings highlight the potential of deep learning for addressing Arabic fake news.

Outlook:

  • Further research can expand the dataset and explore additional embedding techniques for enhanced Arabic fake news detection.
  • These models can be integrated into social media platforms to mitigate the spread of misinformation.
  • Cross-lingual approaches could be investigated to leverage resources from other languages.