INTEGRATING OPTIMAL DEEP LEARNING WITH NATURAL LANGUAGE PROCESSING FOR ARABIC SPAM AND HAM TWEETS RECOGNITION

Najla I. Al-Shathry, Mohammed Alghamdi, Abdullah Saad Al-Dobaian, Abdulbasit A. Darem, Shoayee Dlaim Alotaibi, Manar Almanea, Bandar M. Alghamdi and Shaymaa Sorour
Additional contact information
Najla I. Al-Shathry: Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Mohammed Alghamdi: ��Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia
Abdullah Saad Al-Dobaian: ��Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Abdulbasit A. Darem: �Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia
Shoayee Dlaim Alotaibi: �Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Saudi Arabia
Manar Almanea: ��Department of English, College of Languages and Translation, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
Bandar M. Alghamdi: *Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Shaymaa Sorour: ��†Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-15

Abstract: Natural language processing (NLP) is a domain of artificial intelligence (AI) that concentrates on the communication between human and computer language. Detection of Arabic spam and ham tweets involves leveraging deep learning (DL) models, mainly NLP techniques such as brain-like computing and AI-driven tweets recognition, to mechanically differentiate between spam and ham messages dependent upon content semantics, linguistic patterns, and contextual data within the Arabic text. This study presents an optimal deep learning with natural language processing for Arabic spam and ham tweets recognition (ODLNLP-ASHTR) technique in various complex systems platforms. In the ODLNLP-ASHTR technique, the data pre-processing is initially performed to alter the input tweets into a compatible format, and a BERT word embedding process is used. For Arabic ham and spam tweet recognition, the ODLNLP-ASHTR technique makes use of the self-attention bidirectional gated recurrent unit (SA-BiGRU) model. At last, the detection performance of the SA-BiGRU model can be boosted by the design of an improved salp swarm algorithm (ISSA). The experimental evaluation of the ODLNLP-ASHTR technique takes place using the Arabic tweets dataset. The experimental results pointed out the improved performance of the ODLNLP-ASHTR model compared to recent approaches with a maximum accuracy of 98.11%.

Keywords: Natural Language Processing; Deep Learning; Word Embedding; Salp Swarm Algorithm; Social Networks; Arabic Tweets; Complex Systems (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400523

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