Cyberbullying causes significant harm, especially among adolescents and young adults. With the growth of social media, online harassment through platforms like Facebook and Twitter has also proliferated rapidly. Though social networks have reporting mechanisms, the volume of user-generated content makes manual moderation infeasible. This necessitates automated detection systems that can accurately identify cyberbullying at scale. Recent advances in deep learning provide promising techniques for text classification tasks. This paper explores (CNNs), Recurrent Neural Networks (RNNs), Long Short- Term Memory (LSTM), and transformer models like Bidirectional Encoder Representations from Transformers (BERT) for cyberbullying detection in social networks. The models are evaluated on a benchmark dataset containing 11,000 Facebook comments labeled as clean or cyberbullying. Extensive experiments demonstrate that BERT achieves the highest accuracy of 87.3% followed by a hierarchical (Convolutional Neural Networks-Long Short-Term Memory) CNN-LSTM architecture with 86.5% accuracy. The former benefits from bidirectional context modeling using self-attention while the latter combines the strengths of convolutional layers and LSTMs. The results verify the effectiveness of deep learning methodologies for this problem. However, enhancements in multilingual, multimodal support and adversarial robustness are required. Testing on diverse platforms and content along with user privacy considerations remain as future research directions. This empirical study provides useful insights to build robust cyberbullying detection systems.

1. Abuowaida S., Elsoud E., Al-momani A., Arabiat M., Owida H., Alshdaifat N., and Chan H., “Proposed Enhanced Feature Extraction for Multi-Food Detection Method,” Journal of Theoretical and Applied Information Technology, vol. 101, no. 24, pp. 8140-‏8146, 2023.
2. Agrawal S. and Awekar A., “Deep learning for detecting cyberbullying across multiple social media platforms,” in Proceedings of the European Conference on Information Retrieval, Grenoble, pp. 141-153, 2018.
3. Alazaidah R., Ahmad F., Mohsen M., and Junoh A., “Evaluating Conditional and Unconditional Correlations Capturing Strategies in Multi Label Classification,” Journal of Telecommunication, Electronic and Computer Engineering, vol. 10, no. 2, pp. 47-51, 2018. https://jtec.utem.edu.my/jtec/article/view/4315/3162
4. Alazaidah R., Ahmad F., Mohsin M., and AlZoubi W., “Multi-Label Ranking Method Based on Positive Class Correlations,” Jordanian Journal of Computers and Information Technology, vol. 6, no. 4, pp. 377-391, 2020.
5. Alazaidah R., Almaiah M., and Al-Luwaici M., “Associative Classification in Multi-Label Classification: An Investigative Study,” Jordanian Journal of Computers and Information Technology, vol. 7, no. 2, pp. 166-179, 2021. DOI:10.5455/jjcit.71-1615297634
6. Alazaidah R., Samara G., Almatarneh S., Hassan M., Aljaidi M., and Mansur H., “Multi-Label Classification Based on Associations,” Applied Sciences, vol. 13, no. 8, pp. 5081, 2023. https://doi.org/10.3390/app13085081
7. Al-Garadi A., Varathan K., and Ravana S., “Cybercrime Detection in Online Communications: The Experimental Case of Cyberbullying Detection in the Twitter Network,” Computers and Security, vol. 63, pp. 433-443, 2017. https://doi.org/10.1016/j.chb.2016.05.051
8. Alhusenat A., Owida H., Rababah H., Al-Nabulsi J., and Abuowaida S., “A Secured Multi-Stages Authentication Protocol for IoT Devices,” Mathematical Modelling of Engineering Problems, vol. 10, no. 4,‏ pp. 1352-1358, 2023. https://doi.org/10.18280/mmep.100429
9. Al-luwaici M., Junoh A., AlZoubi W., Alazaidah R., and Al-luwaici W., “New Features Selection Method for Multi-Label Classification Based on the Positive Dependencies among Labels,” Solid State Technology, vol. 63, no. 2, pp. 9896, 2020.
10. Alshraiedeh F., Hanna S., and Alazaidah R., “An Approach to Extend WSDL-Based Data Types Specification to Enhance Web Services Understandability,” International Journal of Advanced Computer Science and Applications, vol. 6, no. 3, pp. 88-98, 2015.
11. Alzyoud M., Alazaidah R., Aljaidi M., Samara G., Qasem M., Khalid M., and Al-Shanableh N., “Diagnosing Diabetes Mellitus Using Machine Learning Techniques,” International Journal of Data and Network Science, vol. 8, no. 1, pp. 179-188, 2024. DOI:10.5267/j.ijdns.2023.10.006
12. Badjatiya P., Gupta S., Gupta M. and Varma V., “Deep Learning for Hate Speech Detection in Tweets,” in Proceedings of the 26^th International Conference on World Wide Web Companion, Perth, pp. 759-760, 2017. https://doi.org/10.1145/3041021.3054223
13. Baroncelli A., Perkins ER., Ciucci E., Frick P., Patrick C., Sica C., “Triarchic Model Traits as Predictors of Bullying and Cyberbullying in Adolescence,” Journal of Interpersonal Violence, vol. 37, no. 5, pp. 3242-3268, 2022. https://doi.org/10.1177/0886260520934448
14. Cheng L., Guo R., and Li J., “Hierarchical Attention Networks for Cyberbullying Detection with Textual and Social Context,” in Proceedings of the 60^th Annual Meeting of the Association for Computational Linguistics, Dublin, pp. 1234-1245, 2022. DOI:10.1137/1.9781611975673.27
15. Dadvar M. and De Jong F., “Cyberbullying Detection: A Step Toward a Safer Internet Yard,” in Proceedings of the 21^st International Conference on World Wide Web, New York, pp. 121-122, 2012. https://doi.org/10.1145/2187980.2187995
16. Devlin J., Chang M., Lee K., and Toutanova K., “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, pp. 4171-4186, 2019. https://doi.org/10.18653/v1/n19-1423
17. Elbasi E. and Zreikat A., “Heart Disease Classification for Early Diagnosis Based on Adaptive Hoeffding Tree Algorithm in IoMT  Data,” The International Arab Journal of Information Technology, vol. 20, no. 1, pp. 38-48, 2023. https://doi.org/10.34028/iajit/20/1/5
18. Hinduja S. and Patchin J., “Bullying, Cyberbullying, and Suicide,” Archives of Suicide Research, vol. 14, no. 3, pp. 206-221, 2010. DOI:10.1080/13811118.2010.494133
19. Hochreiter S. and Schmidhuber J., “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997. DOI:10.1162/neco.1997.9.8.1735
20. Kim Y., “Convolutional Neural Networks for Sentence Classification,” arXiv Preprint, vol. arXiv:1408.5882, pp. 1-6, 2014. https://doi.org/10.48550/arXiv.1408.5882
21. Li Y., Zhang Z., and Wang H., “Multi-task Learning for Cyberbullying Detection and Emotion Recognition in Social Media Texts,” IEEE Transactions on Affective Computing, vol. 14, no. 2, pp. 456-468, 2023.
22. Mazari A. and Kheddar H., “Deep Learning-Based Analysis of Algerian Dialect Dataset Targeted Hate Speech, Offensive Language and Cyberbullying,” International Journal of Computing and Digital Systems, vol. 13, no. 1, pp. 965-972, 2023. DOI:10.12785/ijcds/130177
23. Paul S., Saha S., and Singh J., “COVID-19 and Cyberbullying: Deep Ensemble Model to Identify Cyberbullying from Code-Switched Languages During the Pandemic,” Multimedia Tools and Applications, vol. 82, no. 6, pp. 8773-8789, 2022. DOI:10.1007/s11042-021-11601-9
24. Pericherla S. and Ilavarasan E., “Transformer Network-Based Word Embeddings Approach for Autonomous Cyberbullying Detection,” International Journal of Intelligent Unmanned Systems, vol. 12, no. 1, pp. 154-166, 2021. https://doi.org/10.1108/IJIUS-02-2021-0011
25. Ptáček V., Habernal I., and Hong J., Cyberbullying Classification Dataset. Kaggle. https://www.kaggle.com/datasets/vpacheco/cyberbullying-classification , Last Visited, 2024.
26. Rosa H., Matos D., Ribeiro R., Coheur L., and Carvalho J., “A “Deeper” Look at Detecting Cyberbullying in Social Networks,” in Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, pp. 1-8, 2018. DOI:10.1109/IJCNN.2018.8489211
27. Salmivalli G., “Bullying and the Peer Group: A Review,” Aggression and Violent Behavior, vol. 15, no. 2, pp. 112-120, 2010. https://doi.org/10.1016/j.avb.2009.08.007
28. UNESCO, Behind the Numbers: Ending School Violence and bullying, 2021. https://en.unesco.org/news/behind-numbers-ending-school-violence-and-bullying, Last Visited, 2024.
29. Yadav K., Lamba A., Gupta D., Gupta A., Karmakar P., and Saini S., “Bi-LSTM and Ensemble based Bilingual Sentiment Analysis for a Code-mixed Hindi-English Social Media Text,” in Proceedings of the IEEE 17^th India Council International Conference, New Delhi, pp. 1-6, 2020.