AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets  

作　　者：Dhiaa Musleh Atta Rahman Mohammed Abbas Alkherallah Menhal Kamel Al-Bohassan Mustafa Mohammed Alawami Hayder Ali Alsebaa Jawad Ali Alnemer Ghazi Fayez Al-Mutairi May Issa Aldossary Dalal A.Aldowaihi Fahd Alhaidari 

机构地区：[1]Department of Computer Science,College of Computer Science and Information Technology,Imam Abdulrahman Bin Faisal University,P.O.Box 1982,Dammam,31441,Saudi Arabia [2]Department of Computer Information Systems,College of Computer Science and Information Technology,Imam Abdulrahman Bin Faisal University,P.O.Box 1982,Dammam,31441,Saudi Arabia [3]Department of Networks and Communications,College of Computer Science and Information Technology,Imam Abdulrahman Bin Faisal University,P.O.Box 1982,Dammam,31441,Saudi Arabia

出　　处：《Computers, Materials & Continua》2024年第7期1033-1054,共22页计算机、材料和连续体（英文）

摘　　要：With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关 键 词：Supervised machine learning ensemble learning CYBERBULLYING Arabic tweets NLP 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]