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A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter

Author

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  • Amgad Muneer

    (Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32160, Malaysia)

  • Suliman Mohamed Fati

    (Information Systems Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia)

Abstract

The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00).

Suggested Citation

  • Amgad Muneer & Suliman Mohamed Fati, 2020. "A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter," Future Internet, MDPI, vol. 12(11), pages 1-20, October.
  • Handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:187-:d:437202
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    References listed on IDEAS

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    1. Maher Maalouf, 2011. "Logistic regression in data analysis: an overview," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(3), pages 281-299.
    2. Cynthia Van Hee & Gilles Jacobs & Chris Emmery & Bart Desmet & Els Lefever & Ben Verhoeven & Guy De Pauw & Walter Daelemans & Véronique Hoste, 2018. "Automatic detection of cyberbullying in social media text," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-22, October.
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    Cited by:

    1. Ebrahim A. A. Ghaleb & P. D. D. Dominic & Suliman Mohamed Fati & Amgad Muneer & Rao Faizan Ali, 2021. "The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees," Sustainability, MDPI, vol. 13(15), pages 1-33, July.
    2. José Manuel Ortiz-Marcos & María Tomé-Fernández & Christian Fernández-Leyva, 2021. "Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions," Future Internet, MDPI, vol. 13(1), pages 1-15, January.
    3. Suliman Mohamed Fati & Amgad Muneer & Ayed Alwadain & Abdullateef O. Balogun, 2023. "Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction," Mathematics, MDPI, vol. 11(16), pages 1-21, August.

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