A comparative study of deep learning methods for cyberbullying detection

https://doi.org/10.31130/ud-jst.2025.23(6A).257E

Abstract: 156 | PDF: 76

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Author

  • Dang Thi Kim-Ngan
    The University of Danang - Vietnam-Korea University of Information and Communication Technology, Vietnam
    Nguyen Thi Thanh-Thuy
    The University of Danang - Vietnam-Korea University of Information and Communication Technology, Vietnam
    Lam Mai
    The University of Danang - Vietnam-Korea University of Information and Communication Technology, Vietnam

Keywords:

Cyberbullying detection
social network
natural language processing
machine learning
CNN-biLSTM

Abstract

This paper conducts a comparative study of machine learning and deep learning approaches for cyberbullying detection on social networking platforms. The evaluated models include traditional classifiers such as Logistic Regression and Support Vector Machine (SVM), as well as deep learning architectures including LSTM, BiLSTM, CNN, and a hybrid CNN-BiLSTM model. Experimental results indicate that while SVM and Logistic Regression achieve competitive performance among traditional methods, the proposed CNN-BiLSTM model consistently outperforms others by effectively capturing both local and sequential text features. These findings demonstrate the effectiveness of integrating convolutional and recurrent neural networks in improving the accuracy and robustness of automated cyberbullying detection systems.

References

    [1] B. Dudić, A. Mittelman, and J. Vojtechovský, "Social Media and Marketing Worldwide”, in Proc. Int. Conf. New Technologies, Development and Applications, Cham, Switzerland, 2024, pp. 271–278, Springer Nature
    [2] J. W. Patchin and S. Hinduja, “Measuring Cyberbullying: Implications for Research”, Aggression and Violent Behavior, vol. 23, pp. 69–74, 2015
    [3] J. M. Nagata et al., "Adverse childhood experiences and early adolescent cyberbullying in the United States”, Journal of Adolescence, vol. 95, no. 3, pp. 609–616, 2023
    [4] T. Mahmud, M. Ptaszynski, J. Eronen, and F. Masui, "Cyberbullying detection for low-resource languages and dialects: Review of the state of the art”, Information Processing & Management, vol. 60, no. 5, p. 103454, 2023
    [5] P. Pranathi, V. Revathi, P. Varshitha, S. Shaik, and S. Bhutada, "Logistic regression based cyber harassment identification”, Journal of Advances in Mathematics and Computer Science, vol. 38, no. 8, pp. 76–85, 2023
    [6] I. P. Sari and H. Maulana, "Detecting Cyberbullying on Social Media Using Support Vector Machine: A Case Study on Twitter”, International Journal of Safety and Security Engineering, vol. 13, no. 4, 2023
    [7] M. P. Rao, N. Kota, D. Nidumukkala, M. Madoori, and D. Ali, "Enhancing Online Safety: Cyberbullying Detection with Random Forest Classification”, in Proc. 2024 10th Int. Conf. Commun. Signal Process. (ICCSP), Apr. 2024, pp. 389–393, IEEE
    [8] A. Muneer and S. M. Fati, "A comparative analysis of machine learning techniques for cyberbullying detection on Twitter", Future Internet, vol. 12, no. 11, p. 187, Oct. 2020
    [9] A. Bozyiğit, S. Utku, and E. Nasibov, "Cyberbullying detection: Utilizing social media features", Expert Systems with Applications, vol. 179, Oct. 2021
    [10] L. J. Thun, P. L. Teh, and C.-B. Cheng, "CyberAid: Are your children safe from cyberbullying?", Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 4099–4108, Jul. 2022
    [11] D. Chatzakou et al., "Detecting cyberbullying and cyberaggression in social media”, ACM Transactions on the Web, vol. 13, no. 3, pp. 1–51, 2019
    [12] V. Balakrishnan, S. Khan, T. Fernandez, and H. R. Arabnia, "Cyberbullying detection on Twitter using big five and dark triad features", Personality and Individual Differences, vol. 141, pp. 252–257, Apr. 2019
    [13] C. Iwendi, G. Srivastava, S. Khan, and P. K. R. Maddikunta, "Cyberbullying detection solutions based on deep learning architectures”, Multimedia Systems, vol. 29, no. 3, pp. 1839–1852, 2023
    [14] K. N. Devi et al., "Cyberbullying Detection and Severity Classification Using Bi-LSTM”, in Proc. 2024 9th Int. Conf. Commun. Electronics Syst. (ICCES), Dec. 2024, pp. 338–344, IEEE
    [15] A. Kumar and N. Sachdeva, "A bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media", World Wide Web, vol. 25, no. 4, pp. 1537–1550, Jul. 2022
    [16] A. Kumar and N. Sachdeva, "Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network”, Multimedia Systems, vol. 28, no. 6, pp. 2043–2052, 2022
    [17] P. G. Balaji, P. P. Katariya, S. Sruthi, and M. Venugopalan, "Cyberbullying Detection on Multiclass Data Using Machine Learning and A Hybrid CNN-BiLSTM Architecture”, in Proc. 2024 Int. Conf. Knowledge Eng. Commun. Syst. (ICKECS), vol. 1, pp. 1–6, Apr. 2024, IEEE
    [18] M. Dadvar and K. Eckert, "Cyberbullying detection in social networks using deep learning based models; a reproducibility study", arXiv preprint, arXiv:1812.08046, 2018
    [19] L. Mai, X. Z. Chen, and Y. L. Chen, "Multi-oriented license plate detection based on convolutional neural networks”, in Proc. 2021 Int. Conf. Syst. Sci. Eng. (ICSSE), Aug. 2021, pp. 101–104, IEEE
    [20] Kaggle, "Cyberbullying Classification Dataset”, kaggle.com, [Online]. Available: https://www.kaggle.com/datasets/andrewmvd/cyberbullying-classification. [Accessed: Apr. 22, 2025]
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Published

Jun 30, 2025
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How to Cite
Kim-Ngan, D. T., N. T. Thanh-Thuy, and L. Mai. “A Comparative Study of Deep Learning Methods for Cyberbullying Detection”. The University of Danang - Journal of Science and Technology, vol. 23, no. 6A, June 2025, pp. 115-21, doi:10.31130/ud-jst.2025.23(6A).257E.

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