# The Detection of Fake News in Arabic Tweets Using Deep Learning
###### 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. Arabic language; Twitter; fake news; news credibility; deep learning; word embedding; natural language processing +
Footnote †: journal:  
reduce the impact of fabricated news. These websites depend on human experts to manually confirm or reject the validity of the news [2]. It consumes time and effort to deal manually with a large volume of news and is not scalable. Most recently, the adoption methods of machine learning and deep learning have become popular in tackling anomaly detection problems such as fake news detection [5]. Two types of learning techniques are currently adopted in these methods to construct automated systems for false news detection on social networks, which are news content-based learning and social context-based learning. News content-based methodology mainly focuses on the writing style of the text content of news to discover syntactic or semantic patterns to classify news. The social context-based methodology primarily analyses user behaviour and engagement in social media. Social context features can be explored from the user profile, discussions, and connected networks among users [4]. Informal writing styles, spelling errors, using diverse dialects, etc., makes processing Arabic content on social media more difficult. Other challenges that aggravate the processing complexity are the massive vocabulary and complex morphological patterns of the Arabic language. Moreover, the limited availability of Arabic datasets. These difficulties result in little research focused on detecting and eliminating fake news in Arabic. Few studies have proposed models using deep learning algorithms to identify fabricated news posted on the Twitter platform [3; 5; 6]. In general, these models were trained to target a specific topic of fake news posts and relied only on the textual content of the Tweets to produce a classification. Furthermore, the detection performance of the current models still requires improvement. In an effort to address the aforementioned problems, we proposed a deep learning model that employs news content, text features, and social context, specifically user profile features, to identify the truthfulness of news in Arabic Tweets.