Real Time Cyber bullying Detection with Advanced ML Techniques
Keywords:
Convolutional Neural Networks (CNN), Cyber bullying detection, Deep learning, Machine learning, Natural Language Processing (NLP)Abstract
The rise of digital communication has enhanced connectivity but also increased cybercrimes, particularly cyberbullying, which affects nearly 87% of youth through harassment, revenge acts, and hostile online interactions. To address this, advanced detection mechanisms are essential for safeguarding vulnerable users. This study explores Convolutional Neural Networks (CNNs) for detecting textual cyberbullying, leveraging their ability to capture patterns in text for improved classification accuracy. While CNNs are widely used in Natural Language Processing (NLP), their application in cyberbullying detection enables real-time surveillance and proactive intervention. Unlike traditional methods, CNNs efficiently analyze textual data, enhancing automated detection models. With the growing use of social media, integrating machine learning and deep learning techniques is crucial for developing safer online environments and mitigating the impact of cyberbullying.
References
M. H. Obaida, S. M. Elkaffas, and S. K. Guirguis, "Deep Learning Algorithms for Cyber-Bulling Detection in Social Media Platforms," in IEEE Access, vol. 12, pp. 76901-76908, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3406595.
H. Rosa , N. Pereira,R. Ribeiro, P. C. Ferreira,J. P. Carvalho, “Automatic cyberbullying detection: A systematic review,” Computers in Human Behavior, vol. 93, no. 1, pp. 333–345, Apr. 2019, doi: https://doi.org/10.1016/j.chb.2018.12.021.
R. Zhao, A. Zhou, and K. Mao, “Automatic detection of cyberbullying on social networks based on bullying features,” Proceedings of the 17th International Conference on Distributed Computing and Networking - ICDCN ’16, 2016, doi: https://doi.org/10.1145/2833312.2849567.
P. Badjatiya, S. Gupta, M. Gupta, and V. Varma, “Deep Learning for Hate Speech Detection in Tweets,” Proceedings of the 26th International Conference on World Wide Web Companion - WWW ’17 Companion, 2017, doi: https://doi.org/10.1145/3041021.3054223.
CV Hee ,G. Jacobs, C. Emmery, B. Desmet, E. Lefever, “Automatic detection of cyberbullying in social media text,” PLOS ONE, vol. 13, no. 10, p. e0203794, 2018, doi: https://doi.org/10.1371/journal.pone.0203794.
A.Schmidt and M. Wiegand, “A Survey on Hate Speech Detection using Natural Language Processing,” ACLWeb, Apr. 01, 2017. https://www.aclweb.org/anthology/W17-1101
T. H. Teng and K. D. Varathan, "Cyberbullying Detection in Social Networks: A Comparison between Machine Learning and Transfer Learning Approaches," in IEEE Access, vol. 11, pp. 55533-55560, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3275130.
P. Fortuna and S. Nunes, “A Survey on Automatic Detection of Hate Speech in Text,” ACM Computing Surveys, vol. 51, no. 4, pp. 1–30, Sep. 2018, doi: https://doi.org/10.1145/3232676.
Despoina Chatzakou, N. Kourtellis, J. Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and A. Vakali, “Mean Birds: Detecting Aggression and Bullying on Twitter,” arXiv (Cornell University), Feb. 2017, doi: https://doi.org/10.48550/arxiv.1702.06877.
K. Dinakar, R. Reichart, and H. Lieberman, “Modeling the Detection of Textual Cyberbullying,” Proceedings of the International AAAI Conference on Web and Social Media, vol. 5, no. 3, pp. 11–17, Aug. 2021, doi: https://doi.org/10.1609/icwsm.v5i3.14209
