Authorship Identification for Arabic Texts Using Logistic Model Tree Classification

Hriez, Safaa; Awajan, Arafat

doi:10.1007/978-3-030-52246-9_48

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…Table 1 provides a comprehensive summary of the most frequently utilized style markers offering insight into the core elements to analyze authorship. [3,6,7] Structural [3,15] Authorship classification methods. Numerous studies have applied different methodologies across various languages.…”

Section: Related Workmentioning

confidence: 99%

“…These corpora tailored for AA contribute significantly to the field, expanding its resources. AA research encompasses a diverse array of data, ranging from literary works [2][3][4], news articles [5,8,23,24], tweets [9,19], and blog posts [21]. Albanian, being resource-constrained, has garnered limited attention in NLP.…”

Section: Related Workmentioning

confidence: 99%

“…In today's digital age [1], the Internet has expanded anonymous content, making AA an increasingly concern. This issue carries substantial implications across various domains, including literature [2][3][4], journalism [5][6][7][8], and forensics [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Authorship classification techniques: Bridging textual domains and languages

Misini,

Kadriu,

Canhasi

2024

IJITS JOURNAL

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Authorship classification analyzes an author's prior work to identify their writing style, a unique trait of each language and individual author. This research aims to conduct a thorough comparative analysis of various methods for classifying authorship. The study leverages two corpora: AAALitCorpus of Albanian literary texts and CCAT10 of English columns. We evaluate model-generated features across different configurations. The richness of the features and the breadth of the analysis provide a significant understanding of the problem, setting a new standard for comprehensive linguistic investigations across multiple languages. The study indicates that machine learning algorithms accurately discern authorial writing styles, highlighting the complexities of classifying authorship in a cross-linguistic context.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Authorship classification techniques: Bridging textual domains and languages

Misini,

Kadriu,

Canhasi

2024

IJITS JOURNAL

View full text Add to dashboard Cite

show abstract

“…AA aims to accurately attribute a disputed or unknown text to its. This task is deeply rooted in history since identifying the author of ancient texts has always been the center of attention of linguists [1]. During the last decade, most researchers have addressed text in online social networks, primarily for sentiment analysis [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Arabic authorship attribution on Twitter: what is really matters?

Kah¹,

airej

Zeroual

2022

IJEECS

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Recently, authorship attribution (AA) of online social networks texts has gained more attention. However, since 2015, when the first work that addressed the AA of Arabic tweets was published, we found that nothing much has been done after that. Thus, the current paper presents an extensive study that investigates the effects of various factors on the AA of Arabic short-texts, especially tweets. This led to a proposed architecture in which the AA accuracy is examined depending on the size of the training dataset, the number of classes covered, the text processing techniques applied, the methods used for both feature selection and extraction, and finally, the classifier implemented. As a result, we performed 792 different tests. The highest accuracy recorded is 97.4%, and it is among the best results published so far.

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“…One can also consider the maximum and minimum length of words and the n-gram frequency in the text. This set of structural features is studied(Hriez & Awajan, 2020). For online texts (email messages) can be helpful to consider the separation between paragraphs or the number of blank lines.…”

mentioning

confidence: 99%

A Survey on Authorship Analysis Tasks and Techniques

2022

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Authorship Analysis (AA) is a natural language processing field that examines the previous works of writers to identify the author of a text based on its features. Studies in authorship analysis include authorship identification, authorship profiling, and authorship verification. Due to its relevance, to many applications in this field attention has been paid. It is widely used in the attribution of historical literature. Other applications include legal linguistics, criminal law, forensic investigations, and computer forensics. This paper aims to provide an overview of the work done and the techniques applied in the authorship analysis domain. The examination of recent developments in this field is the principal focus. Many different criteria can be used to define a writer’s style. This paper investigates stylometric features in different author-related tasks, including lexical, syntactic, semantic, structural, and content-specific ones. A lot of classification methods have been applied to authorship analysis tasks. We examine many research studies that use different machine learning and deep learning techniques. As a means of pointing the direction for future studies, we present the most relevant methods recently proposed. The reviewed studies include documents of different types and different languages. In summary, due to the fact that each natural language has its own set of features, there is no standard technique generically applicable for solving the AA problem.

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Authorship Identification for Arabic Texts Using Logistic Model Tree Classification

Cited by 8 publications

References 16 publications

Authorship classification techniques: Bridging textual domains and languages

Authorship classification techniques: Bridging textual domains and languages

Arabic authorship attribution on Twitter: what is really matters?

A Survey on Authorship Analysis Tasks and Techniques

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