Almomani, A, M. O. Nahar, Khalid, Alauthman, Mohammad and Yonbawi, Saud (2023) Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning. Computers, Materials & Continua, 75 (3). pp. 5307-5319. ISSN 1546-2226
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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.
Affiliation: | Skyline University College |
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SUC Author(s): | Almomani, A ORCID: https://orcid.org/0000-0002-8808-6114 |
All Author(s): | Almomani, A, M. O. Nahar, Khalid, Alauthman, Mohammad and Yonbawi, Saud |
Item Type: | Article |
Uncontrolled Keywords: | Cyberbullying; social media; naïve bayes; support vector machine; natural language processing; LDA |
Subjects: | B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 18 Dec 2023 16:02 |
Last Modified: | 18 Dec 2023 16:02 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/705 |
Publisher URL: | https://doi.org/10.32604/cmc.2023.031848 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/37365 |
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