يجب تسجيل الدخول أولا
Cybercrime and Authorship Detection in Very Short Texts: A Quantitative Morpho-Lexical Approach
| المصدر: | مجلة البحث العلمي في الآداب |
|---|---|
| الناشر: | جامعة عين شمس - كلية البنات للآداب والعلوم والتربية |
| المؤلف الرئيسي: | Omar, Abdulfattah (Author) |
| المجلد/العدد: | ع20, ج1 |
| محكمة: | نعم |
| الدولة: |
مصر |
| التاريخ الميلادي: |
2019
|
| الصفحات: | 291 - 316 |
| DOI: |
10.21608/JSSA.2019.38725 |
| ISSN: |
2356-8321 |
| رقم MD: | 978060 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | AraBase |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Authorship Detection | Forensic Linguistics | Morphological Patterns | Lexical Features | Letter Pair Frequencies | Self Organizing Maps (Soms)
|
| رابط المحتوى: |
عدد مرات التحميل
23
| المستخلص: |
The present study proposes an integrated framework that considers letter- pair frequencies / combinations along with the lexical features of documents. Drawing on a quantitative morpho-lexical approach, the study tests the hypothesis that letter information or mapping carries unique stylistic features; and therefore detecting stable word combinations and morphological patterns can be used to enhance the authorship performance in relation to very short texts. The data used for analysis is a corpus of 12240 tweets derived from 87 Twitter accounts. Self-organizing maps (SOMs) model is used for classifying the input patterns that share common features together as a clue that tweets grouped under one class membership are written by the same author. Results indicate that the classification accuracy based on the proposed system is around 76%. Up to 22% of this accuracy was lost, however, when only distinctive words were used, and 26% was lost when the classification performance was based on letter combinations and morphological patterns only. The integration of letter-pairs and morphological patterns had the advantage of improving the accuracy of determining the author of a given tweet. This indicates that the integration of different linguistic variables into an integrated system leads to a better classification performance of very short texts. It is also clear that the use of the self-organizing map (SOM) led to better clustering performance for its capacity to integrate two different linguistic levels of each author profile together. |
|---|---|
| ISSN: |
2356-8321 |
عناصر مشابهة
-
Naive Bayes Classifiers For Authorship Attribution of Arabic Texts
بواسطة: Altheneyan, Alaa Saleh منشور: (2014) -
Arabic Word Processing And Morphology Induction Through Adaptive Memory Self Organisation Strategies
بواسطة: Marzi, Claudia منشور: (2017) -
A Hybrid Semantic Mapping Approach To The Genre Classification Of Literature : The American Novel As An Example
بواسطة: Omar, Abdulfattah منشور: (2018) -
Risks Associated to Cybercrimes
بواسطة: Ghalib, Abdelgadir Warsama منشور: (2023) -
International Cooperation in Combating Cybercrime
بواسطة: Brichi, Belkacem منشور: (2023)
