Cyberbullying Detection on Social Media using Machine Learning

Abstract

Cyberbullying poses a growing concern in digital communication, particularly among younger users. with the rise of social media, especially impacting teens and young adults. Traditional strategies like manual moderation and simple keyword filters often fail to address the complexity of online abuse.. This study presents a real-time detection mechanism for cyberbullying using supervised machine learning models.. The The Random Forest was chosen due to its consistent performance and robustness in classification tasks. in text classification. The model extracts text features using TF-IDF and Bag-of-Words techniques for improved context analysis. from user messages. The system is integrated into a React-based chat app with a Flask backend. Incoming messages from unidentified users are automatically filtered and flagged to protect recipients.. The model also differentiates genuine cyberbullying from casual or friendly exchanges. This reduces incorrect detections, fostering a more secure communication space.. The system offers a dynamic and extensible solution for enhancing online safety across messaging platforms.