Strengthening Cyberbullying Detection with Ensemble Learning - Safeguarding Online Interactions Amongst Youth
Keywords:
Social Networking Platform, Cyberbullying (CB), Machine Learning (ML) Techniques, Ensemble Learning (EL).Abstract
Cyberbullying remains a pressing concern in the digital age, posing significant threats to the well-being of young individuals who engage in online interactions. Social networking platforms, while offering invaluable educational and social benefits, also harbor hidden dangers due to the cloak of anonymity they provide to perpetrators of cyberbullying. This paper presents an innovative machine learning strategy to address the issue of cyberbullying detection on social networking platforms, with a particular focus on enhancing the safety of online interactions among youth. In this study, we propose the application of ensemble learning, a potent method in the realm of machine learning, to enhance the precision and resilience of cyberbullying detection. The motivation behind this choice is two-fold. First, cyberbullying is a multifaceted problem, encompassing a wide range of behaviors and expressions. No single machine learning model can capture the full spectrum of cyberbullying instances effectively. Ensemble learning addresses this limitation by combining the strengths of multiple models, each specializing in different facets of cyberbullying behavior, thereby bolstering the detection process. Second, the intrinsic challenges of identifying cyberbullying, exacerbated by the veil of online anonymity, necessitate a nuanced approach. Ensemble learning, by aggregating the predictions of diverse models, provides an opportunity that will diminish instances of both false positives and false negatives, thereby achieving a more dependable cyberbullying detection system.
Downloads
References
Alam, K. S., Bhowmik, S., & Prosun, P. R. K. (2021, February). Cyberbullying detection: an ensemble based machine learning approach. In 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV) (pp. 710-715). IEEE.
Ahmed, T., Kabir, M., Ivan, S., Mahmud, H., & Hasan, K. (2021, December). Am i being bullied on social media? an ensemble approach to categorize cyberbullying. In 2021 IEEE international conference on big data (Big data) (pp. 2442-2453). IEEE.
Agrawal, S., & Awekar, A. (2018, March). Deep learning for detecting cyberbullying across multiple social media platforms. In European conference on information retrieval (pp. 141-153). Cham: Springer International Publishing.
Raj, M., Singh, S., Solanki, K., & Selvanambi, R. (2022). An application to detect cyberbullying using machine learning and deep learning techniques. SN computer science, 3(5), 401.
Roy, P. K., Singh, A., Tripathy, A. K., & Das, T. K. (2022). Identifying cyberbullying post on social networking platform using machine learning technique. In Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2021 (pp. 186-195). Springer Singapore.
Dalvi, R. R., Chavan, S. B., & Halbe, A. (2020, May). Detecting a Twitter cyberbullying using machine learning. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 297-301). IEEE.
Roy, P. K., Singh, A., Tripathy, A. K., & Das, T. K. (2022). Cyberbullying detection: an ensemble learning approach. International Journal of Computational Science and Engineering, 25(3), 315-324.
Mahmud, M. I., Mamun, M., & Abdelgawad, A. (2022, December). A deep analysis of textual features based cyberbullying detection using machine learning. In 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) (pp. 166-170). IEEE.
Sharma, H. K., & Kshitiz, K. (2018, June). Nlp and machine learning techniques for detecting insulting comments on social networking platforms. In 2018 International conference on advances in computing and communication engineering (ICACCE) (pp. 265-272). IEEE.
Alotaibi, M., Alotaibi, B., & Razaque, A. (2021). A multichannel deep learning framework for cyberbullying detection on social media. Electronics, 10(21), 2664.
Mahat, M. (2021, March). Detecting cyberbullying across multiple social media platforms using deep learning. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 299-301). IEEE.
Chandrasekaran, S., Singh Pundir, A. K., & Lingaiah, T. B. (2022). Deep learning approaches for cyberbullying detection and classification on social media. Computational Intelligence and Neuroscience, 2022.
Singh, N. K., Singh, P., & Chand, S. (2022, November). Deep Learning based Methods for Cyberbullying Detection on Social Media. In 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 521-525). IEEE.
Liu, Y., Zavarsky, P., & Malik, Y. (2019). Non-linguistic features for cyberbullying detection on a social media platform using machine learning. In Cyberspace Safety and Security: 11th International Symposium, CSS 2019, Guangzhou, China, December 1–3, 2019, Proceedings, Part I 11 (pp. 391-406). Springer International Publishing.
Dadvar, M., & Eckert, K. (2020). Cyberbullying detection in social networks using deep learning-based models. In Big Data Analytics and Knowledge Discovery: 22nd International Conference, DaWaK 2020, Bratislava, Slovakia, September 14–17, 2020, Proceedings 22 (pp. 245-255). Springer International Publishing.
Alotaibi, M., Alotaibi, B., & Razaque, A. (2021). A multichannel deep learning framework for cyberbullying detection on social media. Electronics, 10(21), 2664.
Thanigaivel, S., Harshan, S., Syed Shahul Hameed, M., & Umadevi, K. S. (2021). Detection and prevention of cyberbullying using ensemble classifier. In International Virtual Conference on Industry 4.0: Select Proceedings of IVCI4. 0 2020 (pp. 323-333). Springer Singapore.
Ahuja, R., Banga, A., & Sharma, S. C. (2021). Detecting abusive comments using ensemble deep learning algorithms. Malware Analysis Using Artificial Intelligence and Deep Learning, 515-534.
Muneer, A., Alwadain, A., Ragab, M. G., & Alqushaibi, A. (2023). Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT. Information, 14(8), 467.
Shakambhari, Raj, J. S., & Anantha Babu, S. (2022). Smart Cyberbullying detection with Machine Learning. In Disruptive Technologies for Big Data and Cloud Applications: Proceedings of ICBDCC 2021 (pp. 237-248). Singapore: Springer Nature Singapore.
Azeez, N. A., & Fadhal, E. (2023). Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques. Applied Sciences, 13(7), 4570.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
