Open Access

ARTICLE

Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

Khalid M. O. Nahar^1,*, Mohammad Alauthman², Saud Yonbawi³, Ammar Almomani^4,5

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:

Computers, Materials & Continua 2023, 75(3), 5307-5319. https://doi.org/10.32604/cmc.2023.031848

Received 28 April 2022; Accepted 15 September 2022; Issue published 29 April 2023

Abstract

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.

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Keywords

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