# A Transformer-Based Approach to Authorship Attribution in Classical Arabic Texts
## 1 Introduction  
With the widespread availability of textual content online, such as research studies, literary writings, and user-generated text, and in light of the ease of reproducing and sharing these texts, plagiarism and literary theft have become much easier. To help in identifying authors of anonymous or unattributed text and in verifying authorship, literary scholars have been using an approach called \"Authorship Attribution\" (AA). This approach has roots from the 19 th century [1], when [PERSON] [2] made the first recorded attempt to identify an author based on their writing style in 1887. [PERSON] [3] and [PERSON] [4] further explored this in later years using basic statistical methods. However, with technological advancements, new computational methods have been investigated and used to address AA, starting from machine learning approaches and culminating in the use of deep learning and transformer-based approaches. In the early 1960s, the approach of [PERSON] and [PERSON] [5] was considered the foundation of computer-assisted stylometry. Stylometry includes many related fields, such as AA, authorship verification, authorship discrimination, plagiarism detection, and authorship profiling, described as follows [5, 6]:  
* AA is defined as a method of identifying the author of an unknown text. It can be used to detect an author, discriminating between writers' styles, and resolving disputed work that might be attributed to different authors [7]. * Authorship verification involves verifying whether a given text is written by a specific author, and is considered to be a binary classification problem [8]. * Authorship discrimination is used to check whether different texts have the same author [9]. * Plagiarism detection is concerned with content similarity detection; through identifying cases of plagiarism in a given text, often via searching for a sentence or paragraph that has one or more than one writer [9]. * Authorship profiling involves extracting the features of the author's style to determine certain demographics, such as author gender, without identifying the author [5]. AA is well-known as a task in the field of natural language processing (NLP) [10]. According to [PERSON] [11], AA is considered both one of the oldest and most persistent problems in the field of information retrieval; questions have been raised about authenticating documents as long as the documents themselves have existed [11]. AA has gained wide popularity in the literature, because it is key to solving many problems in the areas of authorship forensics, plagiarism detection, and the identification of anonymous authors. The main concept involves recognizing that every author has their own distinct writing style. This is referred to as author's stylometry or stylo-features [12]. Accordingly, AA approaches are concerned with selecting and extracting the best possible features that will provide the best performance of authorship identification using various classification methods [13]. According to [12], AA can be classified into three different categories: one-class attribution, binary-class attribution, and multi-class attribution. In one-class attribution, the purpose is to determine whether a specific document is written by the target author. In binary-class attribution, the purpose is to determine which of two authors wrote the given unattributed documents, based on evidence that the documents in question were written by one of two particular authors. In multi-class attribution, the purpose is to identify whether numerous documents have been written by more than two authors. AA is a research area that aims to attribute a given text to its correct author. Not only literary scholars, but also politicians, historians, forensic experts, and religious scholars may have interests in the issue of authorship [14]. Many studies and research papers have examined the use of AA for the Arabic language [14; 15; 16; 17; 18; 19; 20; 21].