Cyberbullying Detection System on Social Media

This paper presents a cyberbullying detection system designed to identify and mitigate instances of cyberbullying on social media platforms. The system employs advanced machine learning algorithms to analyze user-generated content, including text, images, and interactions, for signs of bullying behavior. By utilizing natural language processing techniques, the system categorizes posts as harmful and enables real-time monitoring and intervention. The goal is to create a safer online environment by providing users, moderators, and platforms with timely alerts and resources to address cyberbullying incidents effectively. This proactive approach aims to reduce the prevalence of cyberbullying and support mental well-being among social media users. Index Terms—Cyberbullying, Machine Learning, Natural Language Processing, Social Media, Sentiment Analysis