Detection of Bulling Massages in Social Media

The rapid growth of social media platforms has facilitated widespread communication, enabling users to express opinions, share information, and engage in conversations. However, the open nature of these platforms has also given rise to the prevalence of cyberbullying, a harmful phenomenon involving the use of electronic communication to harass, intimidate, or demean individuals. This project focuses on the detection of bullying messages in social media to mitigate the adverse effects on users’ well-being and foster a safer online environment. The study employs machine learning algorithms to analyze textual content within chat messages. A meticulously selected dataset, comprising instances of both bullying and non-bullying messages, serves as the foundation for training and validating the detection Ensemble learning approaches are also investigated to further improve the overall performance and reliability of the system. The proposed bullying detection system is designed for seamless integration into existing social media, offering real-time monitoring capabilities to foster a positive and secure digital communication environment. The research contributes to the advancement of cyberbullying mitigation strategies, providing a valuable tool for enhancing user well-being and ensuring a more inclusive online experience.