Cyberbullying Detection and Prevention in Social Media Using Machine Learning and Deep Learning Techniques

This abstract presents a versatile cyberbullying detection and prevention system that enables users to engage through their preferred medium while ensuring a safe online environment. It provides real-time monitoring of social media platforms, empowering users to report and address instances of cyberbullying promptly. The system identifies potentially harmful and cyberbullying content through advanced machine learning algorithms and Natural Language Processing (NLP) techniques and intervenes to prevent further consequences. The system performs a meticulous process of preventing cyberbullying by promoting awareness and issuing warnings, thus, fostering a culture of empathy and respect online. The system also provides analytics and insights on user behavior analysis, enabling proactive measures to be taken to mitigate risks and protect the users. Helpdesk details for support services are also available, ensuring easy and continuous access to assistance and guidance. This cyberbullying detection and prevention system combines various algorithms with a user-friendly interface, providing a safe and supportive online environment.