Arabic Authorship Attribution

Abstract

Law enforcement faces problems in tracing the true identity of offenders in cybercrime investigations. Most offenders mask their true identity, impersonate people of high authority, or use identity deception and obfuscation tactics to avoid detection and traceability. To address the problem of anonymity, authorship analysis is used to identify individuals by their writing styles without knowing their actual identities. Most authorship studies are dedicated to English due to its widespread use over the Internet, but recent cyber-attacks such as the distribution of Stuxnet indicate that Internet crimes are not limited to a certain community, language, culture, ideology, or ethnicity. To effectively investigate cybercrime and to address the problem of anonymity in online communication, there is a pressing need to study authorship analysis of languages such as Arabic, Chinese, Turkish, and so on. Arabic, the focus of this study, is the fourth most widely used language on the Internet. This study investigates authorship of Arabic discourse/text, especially tiny text, Twitter posts. We benchmark the performance of a profile-based approach that uses n -grams as features and compare it with state-of-the-art instance-based classification techniques. Then we adapt an event-visualization tool that is developed for English to accommodate both Arabic and English languages and visualize the result of the attribution evidence. In addition, we investigate the relative effect of the training set, the length of tweets, and the number of authors on authorship classification accuracy. Finally, we show that diacritics have an insignificant effect on the attribution process and part-of-speech tags are less effective than character-level and word-level n -grams.

A Transformer-Based Approach to Authorship Attribution in Classical Arabic Texts

“…Studies on Arabic AA have been conducted on various text genres and forms, such as news articles [15,38], poetry [28], Tweets [16][17][18][19], and classical Arabic texts [14,[20][21][22].…”

“…The SVM outperformed other classifiers, achieving the highest accuracy, 68.67%, on the combined feature set. In another study, Altakrori et al [17] adapted an event visualization tool with Arabic tweets. The researchers compared the profile-based approach based on n-grams as features with instance-based classification techniques using the random forests, SVM, naïve Bayes, and decision trees classification methods with stylometric features.…”

“…Since the performance of the transformer-based models has been proven to be more efficient when they are pretrained on a large number of data items [44], we segmented the books further. This process has been applied in prior AA literature when large texts are involved, as in [17,19]. After splitting the text, we performed simple preprocessing tasks (discussed below).…”

“…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][22]. However, most of these studies have focused on various Arabic collections such as articles, poems, Tweets, and other text genres from Arabic books.…”

Post-Authorship Attribution Using Regularized Deep Neural Network

“…People hide their identities when posting sensitive information [13][14][15]. The delinquents usually masked their identities across multiple networks to circumvent the prowess of text-mining methods [16][17][18]. In addition, the use of proxy internet addresses, hashtags, mentioning of people in texts, and reposting of texts written by others is a common phenomenon [7,12,16].…”