## 1 Introduction  
The Arabic language is spoken by more than 400 million people in 22 countries, including the Kingdom of Saudi Arabia [1], and is the fourth most frequently used language on the World Wide Web [1]. An estimated 58% of the Saudi Arabian population, approximately 19.84 million users [2], are reported to have access to the Internet. Generally, the Arabic language is classified into three main types: classical Arabic, modern standard Arabic (MSA) and Arabic dialect (AD). ADs are defined as the spoken or written variations of the Arabic language used by different Arab regions. Recently, it was noticed that ADs are more frequently used for informal written communication on the Web, owing to the wide range of available social media applications. Language identification, and more recently, dialect identification and discrimination, has become a compelling task in language recognition and natural language processing (NLP). It is the task of automatically classifying the language vocabulary used by a specific community into the geographical region that the native speaker belongs to [3]. This task requires a more fine-grained level of identification, as it is the most challenging language identification task [4]. However, the knowledge obtained from dialect identification is very helpful for many applications, such as document retrieval to classify documents based on their dialect and according to user preferences [5], to complement existing recognition language modellingsystems, and to build a natural language generation system using the generated dialectal mappings [6]. Arabic dialect identification (ADI) has been receiving increasingly significant attention in recent years. Early works on ADI focused on either distinguishing the dialects from MSA or among dialect countries [5,7]. According to a recent review [8], fine-grained dialects have not been thoroughly investigated in the ADI literature. Recently, [PERSON] et al. [9] created a fine-grained dialects dataset that included 25 cities from a number of different Arab countries. Their results were one of the main motivations of this study, to investigate the problem of fine-grained Arabic language dialects that only included dialects from very close regions in one country, using a short sentence that consisted of a few words, and without the notion of a word. The use of character-model machine learning models, when building either classic machine learning or deep learning models, in fact shows a better applicability in the field of natural language processing (NLP) over word-level models [10,11]. This is due to the fact that the word-level model has a number of shortcomings. First, it represents each word separately as a token and as words sharing a common root, with the prefix or suffix treated as separate words. Thus, making the word-level model is statistically inefficient. Second, the list of vocabulary in the word-level model is fixed in the training corpus, and when the model is tested on unseen words, it usually fails to handle it. So, it cannot handle small changes in words such as small differences in characters between fine-grained dialects. Character-level models, on the other hand, are effectively unrestricted in their vocabulary. On this matter, a number of researchers have discovered that the character-level model can overcome word-level model issues in text classification problems, as long as the text is represented as a sequence of one-hot vectors, and without changing the machine learning models. Moreover, character-level models help to minimise the steps needed for data preprocessing compared to word-level models, and the Arabic language is indeed a very challenging language. Based on this intuition, the character-level model was investigated in this study to solve the problem of ADI, more precisely for fine-grained dialects on regions from the same country, and the Saudi dialects were chosen as a case in this study, as each dialect has unique phrases or words that could be very informative.