A Deep Learning Framework for Arabic Cyberbullying Detection in Social Networks  

作　　者：Yahya Tashtoush Areen Banysalim Majdi Maabreh Shorouq Al-Eidi Ola Karajeh Plamen Zahariev 

机构地区：[1]Computer Science Department,Jordan University of Science and Technology,Irbid,22110,Jordan [2]Department of Information Technology,Faculty of Prince AI-Hussein Bin AbdallahⅡFor Information Technology,The Hashemite University,Zarqa,13133,Jordan [3]Computer Science Department,Tafila Technical University,Tafila,6110,Jordan [4]Department of Digital Media Software Engineering,Ferris State University,Big Rapids,MI 49503,USA [5]Department of Telecommuniations,University of Ruse“Angel Kanchev”,Ruse,7017,Bulgaria

出　　处：《Computers, Materials & Continua》2025年第5期3113-3134,共22页计算机、材料和连续体(英文)

基　　金：financed by the European Union-NextGenerationEU,through the National Recowery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001-C01.

摘　　要：Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.

关 键 词：Arabic text lassification arabic text mining cyberbullying detection neural networks deep learning CNN LSTM YOUTUBE Bi-LSTM 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]