The Detection of Fake News in Arabic Tweets Using Deep Learning

Author:

Alyoubi Shatha1,Kalkatawi Manal2ORCID,Abukhodair Felwa2ORCID

Affiliation:

1. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 21911, Saudi Arabia

2. Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Abstract

Fake news has been around for a long time, but the rise of social networking applications over recent years has rapidly increased the growth of fake news among individuals. The absence of adequate procedures to combat fake news has aggravated the problem. Consequently, fake news negatively impacts various aspects of life (economical, social, and political). Many individuals rely on Twitter as a news source, especially in the Arab region. Mostly, individuals are reading and sharing regardless of the truth behind the news. 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. Therefore, creating an automatic system for recognizing news credibility on social networks relying on artificial intelligence techniques, including machine learning and deep learning, has attracted the attention of researchers. Using deep learning methods has shown promising results in recognizing fake news written in English. Limited work has been conducted in the area of news credibility recognition for the Arabic language. This work proposes a deep learning-based model to detect fake news on Twitter. The proposed model utilizes the news content and social context of the user who participated in the news dissemination. In seeking an effective 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 a superior performance in terms of accuracy and an F1-score of 0.956. This finding proves that the proposed model accurately detects fake news in Arabic Tweets relating to various topics.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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