International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | To Implement Cyberbullying Detection on Social Networks Using Machine Learning |
|---|---|
| ABSTRACT | Cyberbullying has emerged as a serious social issue with the rapid growth of social media and online communication platforms. It negatively impacts users’ mental health, emotional well-being, and overall online safety. Traditional cyberbullying detection systems mainly rely on keyword matching or basic sentiment analysis, which often fail to understand context, sarcasm, and non-textual content such as images and GIFs. The proposed system considers five major feature categories, including linguistic, contextual, behavioural, visual, and metadata features, to accurately identify cyberbullying content. It supports detection from text posts, images, and GIFs, enabling comprehensive analysis of modern social media interactions. To achieve this, the system will be developed using the Python programming language and the Django framework, ensuring a robust and secure backend architecture |
| AUTHOR | DR.NAVEEN BILANDI. P, NOORJAHAN. D, PRITHIKA.V, RUBA DHARSHINI. D.M Associate Professor, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India UG Scholar, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404082 |
| pdf/82_To Implement Cyberbullying Detection on Social Networks Using Machine Learning.pdf | |
| KEYWORDS | |
| References | [1] Yadav, J., Kumar, D., & Chauhan, D. “Cyberbullying Detection using Pre-Trained BERT Model,” 2020. [2] Chen, S., Wang, J., & He, K. “Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model,”2024. [3] Cuzzocrea, A., Akter, M. S., Shahriar, H., & García Bringas, P. “Cyberbullying Detection, Prevention, and Analysis on Social Media via Trustable LSTM-Autoencoder Networks over Synthetic Data,” 2025. [4] Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. A Review on Deep-Learning-Based Cyberbullying Detection,” 2023. [5] Nitya Harshitha, T., Prabu, M., Suganya, E., Sountharrajan, S., Bavirisetti, D. P., Gadde, N., & Uppu, L. S. “ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media,” 2024. |
