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Arabic Fake News Detection Based on Textual Analysis.

Hanen Himdi¹, George Weir¹, Fatmah Assiri²

¹Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK.

Arabian Journal for Science and Engineering

|February 23, 2022

Summary

This study introduces a machine learning model for detecting Arabic fake news, outperforming human accuracy. It also presents the first crowdsourced dataset for Arabic fake news detection.

Keywords:

Deceptive text Fake news Machine learning Natural language processing

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Area of Science:

Natural Language Processing
Machine Learning
Computational Linguistics

Background:

Social media facilitates rapid information spread, leading to the proliferation of fake news with societal implications.
Detecting fake news is crucial, yet research on Arabic fake news detection remains limited.
Existing studies often lack Arabic language support, highlighting a significant research gap.

Discussion:

This paper addresses the challenge of Arabic fake news detection through textual analysis.
A supervised machine learning model is proposed to classify the credibility of Arabic news articles.
The study introduces a novel approach for feature extraction using Arabic lexical wordlists and a dedicated Natural Language Processing tool.

Key Insights:

The developed model demonstrates high accuracy in identifying fake news in Arabic.
The creation of the first crowdsourced dataset for Arabic fake news is a significant contribution.
The proposed textual feature extraction method is effective and innovative.

Outlook:

This research paves the way for more robust fake news detection systems in Arabic.
The findings can be applied to enhance media credibility and combat misinformation.
Further research can explore multilingual fake news detection and advanced deep learning techniques.