Author
Listed:
- MUHAMMAD SWAILEH A. ALZAIDI
(Department of English Language, College of Language Sciences, King Saud University, P.O. Box 145111, Riyadh, Saudi Arabia)
- FAHEED A. F. ALRSLANI
(��Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia)
- ALYA ALSHAMMARI
(��Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)
- MAJDY M. ELTAHIR
(�Department of Information Systems, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia)
- HANAN AL SULTAN
(�Department of English, College of Arts, King Faisal University, Hofuf, Saudi Arabia)
- AHMED S. SALAMA
(��Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt)
Abstract
The Arab nation is seriously affected by computational propaganda. The detection of Arab computational propaganda has become a hot research topic in social networking platforms. Propaganda campaigns endeavor to influence people’s mindsets to improve a particular agenda. They automatically employ the anonymity of the Internet, the micro-profiling capability of social network platforms, and the ease of managing and creating coordinated networks to reach masses of social network users with persuasive messages, mainly aimed at topics each user is sensitive to, and ultimately affecting the outcomes on the targeted problem. Using computation techniques and methods, analysts and researchers can better understand the scope, scale, and impact of propaganda efforts in Arabic-speaking communities and develop strategies to counter them. In recent times, deep learning (DL) approaches targeted explicitly at analyzing, detecting, or countering propaganda within online platforms or Arabic-speaking communities. DL is a subset of machine learning (ML), which includes training artificial neural networks (ANNs) with multiple layers for learning data representation. This paper designs an improved fractal walrus optimization algorithm with DL-based Arab computation propaganda detection (IWOADL-ACPD) technique. The IWOADL-ACPD method mainly focuses on the recognition and classification of propaganda in the Arabic language. The IWOADL-ACPD method begins with a preprocessing step to standardize and clean raw Arabic text data. Consequently, BERT word embedding encodes meaningful data, capturing contextual nuances vital for accurately detecting propaganda. In addition, the stacked sparse autoencoder (SSAE) detection technique is employed to discern subtle patterns indicative of propaganda content. To improve the performance of the SSAE method, the IWOADL-ACPD method uses IWOA to fine-tune the hyperparameter effectively. The proposed IWOADL-ACPD method contributes to Arabic computation propaganda detection by providing an adaptive and comprehensive technique for the complexity of cultural, digital, and linguistic landscapes specific to the Arabic-speaking context. The robustness and efficacy of the IWOADL-ACPD technique are demonstrated through stimulation analysis on the Arabic dataset, which showcases its capability to perform better than other existing methods. The IWOADL-ACPD technique exhibited a superior accuracy value of 95.25% over existing methods.
Suggested Citation
Muhammad Swaileh A. Alzaidi & Faheed A. F. Alrslani & Alya Alshammari & Majdy M. Eltahir & Hanan Al Sultan & Ahmed S. Salama, 2025.
"Computational Insights Into Arabic Propaganda: An Integration Of Corpus Linguistics With Deep Learning Approach,"
FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 33(02), pages 1-15.
Handle:
RePEc:wsi:fracta:v:33:y:2025:i:02:n:s0218348x25400195
DOI: 10.1142/S0218348X25400195
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:fracta:v:33:y:2025:i:02:n:s0218348x25400195. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/fractals .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.