Document Details

Document Type : Thesis 
Document Title :
DETECTION OF FAKE NEWS IN SOCIAL NETWORKS USING DEEP LEARNING: ARABIC TWEETS AS AN EXAMPLE
كشف الأخبار الزائفة في الشبكات الأجتماعية بإستخدام منهج التعلم العميق: التغريدات العربية كعينة
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : Fake news has been around for a long time, but the rise of social networking ap- plications over recent years has rapidly increased the growth of fake news among individuals. The absence of adequate procedures to combat fake news has aggra- vated the problem. Identifying fake news manually on these open platforms would be challenging as they allow anyone to build networks and publish the news in real-time. Many individuals rely on Twitter as a news source, especially in the Arab region. Mostly, they are reading and sharing regardless of the truth behind the news. Therefore, creating an automatic system for identifying news credibility on Twitter relying on artificial intelligence techniques, including machine learning and deep learning, has attracted the attention of researchers. Using deep learning methods has shown impressive results in identifying fake news written in English language. However, limited work was conducted in the area of news credibility detection for Arabic language. This work proposes a deep learning-based model that utilizes news content and social context features to identify fake news on Twitter. In seeking to find an efficient detection model for fake news, we performed extensive experiments using two deep learning algorithms with varying word embedding models. The experiments were evaluated using a self-created dataset. The experimental results revealed that the MARBERT with the Convolutional Neural Network (CNN) model scores spectacular performance with accuracy and an F1-score of 0.956. This finding proved that the proposed model accurately detects fake news in Arabic tweets relating to various topics. 
Supervisor : Dr. Manal Kalkatawi 
Thesis Type : Master Thesis 
Publishing Year : 1445 AH
2023 AD 
Added Date : Friday, November 10, 2023 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
شذى عبدالرحيم اليوبيAlyoubi, Shatha AbdulrahimResearcherMaster 

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