Year: 2025
Type: conference-paper
Abstract: The significant rise in social media usage has brought attention to the critical issue of fake news, which has become a prominent subject of academic and professional research in recent years.The detection of fake news written in Arabic presents distinct challenges due to the language's structural complexity and instances of homography.These factors contribute to additional difficulties in analyzing the text and interpreting its meanings effectively.This research paper aims to improve the detection of fake news in Arabic.The authors employed various datasets sourced from multiple platforms.They implemented different machine learning and deep learning algorithms, such as Support Vector Machine (SVM) and Naive Bayes (NB), in addition to Long Short-Term Memory (LSTM) Networks and AraBERT.The results were analyzed, and the findings indicated promising outcomes, demonstrating enhanced accuracy in identifying fake news in Arabic.
Authors: Abdallah Qusef, Hatem Mosa, Ammar El-Hassan
Institution: Princess Sumaya University for Technology
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414420385