Authorship Attribution of Arabic Articles

Hajja, Maha; Yahya, Ahmad Zahiruddin Bin; Yahya, Adnan

doi:10.1007/978-3-030-32959-4_14

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…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].…”

Section: Related Workmentioning

confidence: 99%

“…Their study shows that Light 10 achieved 67% accuracy; however, Khoga achieved 64%, while the accuracy rates of the GOLD and non-stemmed datasets were 61% and 78%, respectively. Hajja et al [15] conducted an additional study in which they analyzed modern standard Arabic articles (70 articles by seven authors). They investigated the impact of certain defined text features on the authors' styles.…”

Section: Related Workmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

“…However, this technique requires experts to extract those features, which is considered to be costly and time-consuming. Surveying and reviewing the literature [15,16,28] has revealed that most studies have used the traditional machine learning classifiers, such as naïve Bayes, support vector machine (SVM), SMO-SVM, linear discriminant analysis (LDA), K-nearest-neighbors (KNN), logistic regression, and the Gaussian Bayes and decision trees classifiers. More recent ML approaches have shifted to using deep learning methods.…”

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confidence: 99%

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

AlZahrani,

Al-Yahya

2023

Applied Sciences

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Authorship attribution (AA) is a field of natural language processing that aims to attribute text to its author. Although the literature includes several studies on Arabic AA in general, applying AA to classical Arabic texts has not gained similar attention. This study focuses on investigating recent Arabic pretrained transformer-based models in a rarely studied domain with limited research contributions: the domain of Islamic law. We adopt an experimental approach to investigate AA. Because no dataset has been designed specifically for this task, we design and build our own dataset using Islamic law digital resources. We conduct several experiments on fine-tuning four Arabic pretrained transformer-based models: AraBERT, AraELECTRA, ARBERT, and MARBERT. Results of the experiments indicate that for the task of attributing a given text to its author, ARBERT and AraELECTRA outperform the other models with an accuracy of 96%. We conclude that pretrained transformer models, specifically ARBERT and AraELECTRA, fine-tuned using the Islamic legal dataset, show significant results in applying AA to Islamic legal texts.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

AlZahrani,

Al-Yahya

2023

Applied Sciences

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“…Nowadays, computational stylometry has a wide range of applications in literary science [15], forensics [3,4,37], social media analysis [7,9,18,19], psycholinguistics [30,32], and even source code analysis [2,13]. Computational stylometry has been also explored in different languages including Chinese [45], Arabic [17,21], Russian [27,31], and Turkish [1], among others [22,53].…”

Section: Related Workmentioning

confidence: 99%

Unifying Lexical, Syntactic, and Structural Representations of Written Language for Authorship Attribution

Jafariakinabad

Hua

2021

SN COMPUT. SCI.

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Writing style in written language is a combination of consistent decisions associated with a specific author at different levels of language production, including lexical, syntactic, and structural. The recent work in neural network based style analysis mainly lacks the multi-level modeling of writing style. In this paper, we introduce a style-aware neural model to encode document information from three stylistic levels and evaluate it in the domain of authorship attribution. First, we propose a simple way to jointly encode syntactic and lexical representations of sentences. Subsequently, we employ an attention-based hierarchical neural network to encode the syntactic and semantic structure of sentences in documents while rewarding the sentences which contribute more in capturing the writing style. Our experimental results, based on four benchmark datasets, reveal the benefits of encoding document information from all three stylistic levels when compared to the baseline methods in the literature. Additionally, We adopt a transfer learning approach and use deep contextualized word representation (ELMo) in our model to measure the impact of lower level linguistic representations versus higher level linguistic representations of ELMo in the task of authorship attribution. According to our experimental results, lower level linguistic representations which mainly carry syntactic information demonstrate better performance in authorship attribution task when compared to higher level linguistic representations which mainly carry semantic information.

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Unveiling Deception in Arabic: Optimization of Deceptive Text Detection Across Formal and Informal Genres

Alhayan,

Himdi,

Alharbi

2024

IEEE Access

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Authorship Attribution of Arabic Articles

Cited by 6 publications

References 8 publications

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

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

Unifying Lexical, Syntactic, and Structural Representations of Written Language for Authorship Attribution

Unveiling Deception in Arabic: Optimization of Deceptive Text Detection Across Formal and Informal Genres

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