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

Document Type : Proceedings of The 6'th International Conference on Communication Management and Information Technology (ICCMIT'20)


1 Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.

2 Information Technology Dept., College of Computer, Qassim University, Qassim, Saudi Arabia.


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.


Alharbi, B. Y., Alharbi, M. S., Alzahrani, N. J., Alsheail, M. M., Alshobaili, J. F., & Ibrahim, D. M. (2019). Automatic Cyber Bullying Detection in Arabic Social Media. Int J Engineering Research and Technology, 12(12), 2330-2335.
Feng, J., Gong, C., Li, X., & Lau, R. Y. (2018). Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews. Wireless Communications and Mobile Computing, 2018.
Gamal, D., Alfonse, M., El-Horbaty, E. S. M., & Salem, A. B. M. (2019). Twitter benchmark dataset for Arabic sentiment analysis. Int J Mod Educ Comput Sci, 11(1), 33.
Haidar, B., Chamoun, M., & Serhrouchni, A. (2018, September). Arabic Cyberbullying Detection: Using Deep Learning. In 2018 7th International Conference on Computer and Communication Engineering (ICCCE) (pp. 284-289). IEEE.
Mangaonkar, A., Hayrapetian, A., & Raje, R. (2015, May). Collaborative detection of cyberbullying behavior in Twitter data. In 2015 IEEE international conference on electro/information technology (EIT) (pp. 611-616). IEEE.
Number Cruncher Statistical Systems (NCSS), (2007) “Statistical system for Windows,”.
Nurrahmi, H., & Nurjanah, D. (2018, March). Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility. In 2018 International Conference on Information and Communications Technology (ICOIACT) (pp. 543-548). IEEE.
Pradheep, T., Sheeba, J. I., Yogeshwaran, T., & Pradeep Devaneyan, S. (2017, December). Automatic Multi Model Cyber Bullying Detection from Social Networks. In Proceedings of the International Conference on Intelligent Computing Systems (ICICS 2017–Dec 15th-16th 2017) organized by Sona College of Technology, Salem, Tamilnadu, India.
Rosa, H., Matos, D., Ribeiro, R., Coheur, L., & Carvalho, J. P. (2018, July). A “deeper” look at detecting cyberbullying in social networks. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Van Hee, C., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., Verhoeven, B., ... & Hoste, V. (2018). Automatic detection of cyberbullying in social media text. PloS one, 13(10).
Zhao, R., & Mao, K. (2016). Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Transactions on Affective Computing, 8(3), 328-339.
Volume 12, Issue 2
Proceedings of The 6'th International Conference on Communication Management and Information Technology (ICCMIT'20)
Pages 123-130