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Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

Khalid M. O. Nahar1,*, Mohammad Alauthman2, Saud Yonbawi3, Ammar Almomani4,5

1 Computer Science Department, Yarmouk University, Irbid, Jordan
2 Department of Information Security, Faculty of Information Technology, University of Petra, Amman, Jordan
3 Software Engineering Department, University of Jeddah, Jeddah, KSA
4 Research and Innovation Department, Skyline University College, P.O. Box 1797, Sharjah, UAE
5 IT Department-Al-Huson University College, Al-Balqa Applied University, P. O. Box 50, Irbid, Jordan

* Corresponding Author: Khalid M. O. Nahar. Email: email

Computers, Materials & Continua 2023, 75(3), 5307-5319.


Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as latent Dirichlet allocation. In addition, we used accuracy, precision, recall, and F1 score to assess prior classifiers. Results show that the use of logistic regression, support vector machine, random forest model, and Naïve Bayes has 95%, 94.97%, 94.66%, and 93.1% accuracy, respectively.


Cite This Article

K. M. O. Nahar, M. Alauthman, S. Yonbawi and A. Almomani, "Cyberbullying detection and recognition with type determination based on machine learning," Computers, Materials & Continua, vol. 75, no.3, pp. 5307–5319, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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