Using Machine Learning Algorithms for Automatic Cyber Bullying Detection in Arabic Social Media

• Main Authors: Bedoor Y. AlHarbi, Mashael S. AlHarbi, Nouf J. AlZahrani, Meshaiel M. Alsheail, Dina M. Ibrahim
• Format: Article
• Language: fas
• Published: University of Tehran 2020-06-01
• Series: Journal of Information Technology Management
• Subjects: cyberbullying; machine learning (ml); sentiment analysis; cyberbullying detection in arabic
• Online Access: https://jitm.ut.ac.ir/article_75796_38e99c269fdd70ebb3d0484afa88f3f2.pdf
• ISSN: 2008-5893; 2423-5059

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine learning algorithms. In this paper, we have reviewed algorithms for automatic cyberbullying detection in Arabic of machine learning, and after comparing the highest accuracy of these classifications we will propose the techniques Ridge Regression (RR) and Logistic Regression (LR), which achieved the highest accuracy between the various techniques applied in the automatic cyberbullying detection in English and between the techniques that was used in the sentiment analysis in Arabic text, The purpose of this work is applying these techniques for detecting cyberbullying in Arabic.