## 1 Introduction  
News has been a fundamental part of human history since its inception. It provides us with a window into the current events of the world and, thus, an understanding of the world around us. Interest in the news is pervasive nowadays, mainly because digitization has allowed individuals to follow news online despite their busy schedules. Due to the abundance of articles released daily, people may concentrate on issues connected to their interests in geopolitics, religion, and work. For news consumers, obtaining reliable information is crucial given that it influences their views and potential reactions regarding events in their communities [1]. For example, [PERSON] and [PERSON] [2] researched how financial breaking news affected the price of stock markets, discovering that prices remained just as they were 20 min before the release of news articles and increased after the news release. Freely available open data, information, and news from diverse sources have been crucial areas associated with the rise of the Internet. Recently, for example, with increasing concerns regarding COVID-19, several health organizations published daily and real-time updates about the global spread of the virus. The news articles were published and circulated globally by various established news agencies and some online news websites. The accuracy of the news published was paramount due to its enormous impact on the public and society. Verification of the news sources became necessary for the readers at that time due to the high level of fake news distributed, which caused panic and overwhelmed governments. Therefore, verifying the source of news articles to determine the credibility of the news can play an essential role in preventing the spread of inaccurate information, which may have severely negative consequences for the public, law enforcement agencies, and other stakeholders in society. Readers are concerned with verifying the source of news since it can influence their perspective on specific topics. To achieve this, automating the identification of news sources is imperative. Natural language processing, machine learning, and deep learning approaches can provide effective alternatives to the manual classification of text in specific categories so that identifying news articles' origins can be achieved. Assigning news articles to a specific country can be achieved through the use of classification models. Although there exists much literature on text classification topics, such as fake news classification [1; 3], to our knowledge, the verification of the source of news articles in terms of their originating country has received little attention in the research domain. For example, [PERSON] and [PERSON] [4] built a classifier that classified English news articles based on their city of origin. The classifier achieved 85% accuracy and was trained on the word parts of speech as textual features. Identifying the source of news in languages for which there are few resources, such as Arabic, is more challenging. In this regard, the main contributions of this work are three-fold:  
* Proposal of an automated approach for classifying Arabic news articles based on the country of origin using ML and DL models. * Application of various ML and DL models combined with different feature extraction methods to comprehensively evaluate classification performance (see Figure 1). * Building of a large Arabic dataset that includes news articles about Brexit. The novel contribution of our work is that we rely on the textual content of the articles to identify the articles' country of origin without any associated metadata, such as author name, date, or newspaper name. This paper starts with a literature review and then progresses to current research detailing the development of a classification model that classifies Arabic news articles based on their country of origin. The remainder of this work is organized in the following manner: In Section 2, we briefly review related research on news classification.