TY  - EJOU
AU  - Nahar, Khalid M. O. 
AU  - Alauthman, Mohammad 
AU  - Yonbawi, Saud 
AU  - Almomani, Ammar 

TI  - Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 75
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - Cyberbullying; social media; naïve bayes; support vector machine; natural language processing; LDA

DO  - 10.32604/cmc.2023.031848