Analysis of the Effectiveness of Cyberbullying Early Detection Systems on Social Media Using Deep Learning Models
Keywords:
Cyberbullying Detection, Deep Learning, Social Media, BERT, Early InterventionAbstract
The proliferation of social media platforms has significantly transformed human communication but has also intensified the prevalence of cyberbullying, posing serious psychological and emotional risks, particularly among adolescents and young adults. This study investigates the effectiveness of early detection systems for cyberbullying using deep learning models, with a comparative analysis of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and the transformer-based BERT (Bidirectional Encoder Representations from Transformers). Utilizing publicly available social media datasets, the research focuses on evaluating each model’s performance in terms of accuracy, precision, recall, and F1-score, with particular emphasis on the early stages of harmful message propagation. The findings reveal that BERT significantly outperforms traditional models in detecting context-dependent and implicit forms of cyberbullying, while LSTM proves effective in handling sequential data. However, the enhanced performance of deep learning models comes at the cost of increased computational complexity. The study concludes that a hybrid detection framework may offer an optimal balance between accuracy and efficiency for real-time applications. This research contributes to the development of scalable and context-aware cyberbullying detection systems, providing valuable insights for social media platforms, developers, and policymakers in fostering safer online environments.
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References
Aburomman, A. A., & Reaz, M. B. I. (2017). A survey of intrusion detection systems based on ensemble and hybrid classifiers. Computers & Security, 65, 135–152.
Alexandropoulos, S.-A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019). Data preprocessing in predictive data mining. The Knowledge Engineering Review, 34, e1.
Banks, J. (2010). Regulating hate speech online. International Review of Law, Computers & Technology, 24(3), 233–239.
Campfield, D. C. (2008). Cyber bullying and victimization: Psychosocial characteristics of bullies, victims, and bully/victims. University of Montana.
Chen, M., Weinberger, K. Q., & Sha, F. (2013). An alternative text representation to tf-idf and bag-of-words. ArXiv Preprint ArXiv:1301.6770.
Choi, D., Matni, Z., & Shah, C. (2016). What social media data should i use in my research?: A comparative analysis of twitter, youtube, reddit, and the new york times comments. Proceedings of the Association for Information Science and Technology, 53(1), 1–6.
Costello, E. J. (2016). Early detection and prevention of mental health problems: developmental epidemiology and systems of support. Journal of Clinical Child & Adolescent Psychology, 45(6), 710–717.
David, M. M., Kwambo, D. Z., Clement, G. P., & Dami, B. E. (2019). Self-esteem, posttraumatic stress disorder and suicidal ideation among victims of sexual violence. Journal of Psychology & Clinical Psychiatry, 4, 147–154.
Deng, L. (2012). Three classes of deep learning architectures and their applications: a tutorial survey. APSIPA Transactions on Signal and Information Processing, 57, 58.
Jahan, M. (2020). Cyber bullying identification and tackling using natural language processing techniques. M. Jahan.
Kamara, A. M. (2020). The role of anonymous content type in cyberbullying. Colorado Technical University.
Kumar, A., & Sachdeva, N. (2019). Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis. Multimedia Tools and Applications, 78(17), 23973–24010.
Livingstone, S., Stoilova, M., & Kelly, A. (2016). Cyberbullying: Incidence, trends and consequences. Ending the Torment: Tackling Bullying Form the Schoolyard to Cyberspace. United Nations Special Representative of the Secretary-General on Violence against Children, 115–120.
Myung, J. I., & Pitt, M. A. (2004). Model comparison methods. Methods in Enzymology, 383, 351–366.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1.
Nikiforos, S., Tzanavaris, S., & Kermanidis, K.-L. (2020). Virtual learning communities (VLCs) rethinking: influence on behavior modification—bullying detection through machine learning and natural language processing. Journal of Computers in Education, 7, 531–551.
Patchin, J. W., & Hinduja, P. D. S. (2020). Tween cyberbullying. Cyberbullying Research Center: Jupiter, FL, USA.
Talpur, B. A., & O’Sullivan, D. (2020). Multi-class imbalance in text classification: A feature engineering approach to detect cyberbullying in twitter. Informatics, 7(4), 52.
Vasilev, I. (2019). Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch. Packt Publishing Ltd.
Walter, F. M., Thompson, M. J., Wellwood, I., Abel, G. A., Hamilton, W., Johnson, M., Lyratzopoulos, G., Messenger, M. P., Neal, R. D., & Rubin, G. (2019). Evaluating diagnostic strategies for early detection of cancer: the CanTest framework. Bmc Cancer, 19(1), 1–11.
Wu, Y., Wu, C., Li, B., Zhang, L., Li, Z., & Lau, F. C. M. (2014). Scaling social media applications into geo-distributed clouds. IEEE/ACM Transactions On Networking, 23(3), 689–702.
Xu, Y., & Goodacre, R. (2018). On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. Journal of Analysis and Testing, 2(3), 249–262.
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235–1270.
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