CYBERBULLYING DETECTION SYSTEM USING MACHINE LEARNING

Abstract: Cyberbullying has become a serious issue on social media platforms, affecting individuals emotionally and psychologically through harmful messages, images, and audio content. Traditional systems for detecting cyberbullying are limited as they mainly focus on keyword-based text analysis and fail to understand context or handle multimedia data effectively. To overcome these limitations, this project proposes an intelligent cyberbullying detection system using advanced machine learning and deep learning techniques. Overall, the proposed system improves the accuracy, efficiency, and scalability of cyberbullying detection by combining multimodal data analysis and modern AI techniques, making it a practical solution for enhancing online safety and digital well-being.

Keywords: Cyberbullying Detection, Machine Learning, Natural Language Processing, BERT, Convolutional Neural Network, Image Processing, Speech Recognition, Multimodal Analysis, Social Media Analysis, Real-Time Detection