Automated Detection and Response to Cyberbullying Using Machine Learning
Salmi Younes, Debabha Ramzi, Salem Osman, Mehaoua Ahmed · 2023
Cyberbullying poses a significant threat to the mental and emotional well-being of individuals, particularly in today’s digital age, where online communication and social media are pervasive. Current automated content moderation solutions often struggle to effectively detect and filter offensive messages. In this paper, we present an innovative bot designed to combat cyberbullying by detecting and reporting insults in both voice and text messages. Our bot utilizes OpenAI’s API, speech recognition technology and machine learning models to detect cyberbullying. We discuss the challenges related to detecting and preventing cyberbullying, the technologies and methodologies employed in our bot, and the results of its performance in detecting and filtering offensive messages. The proposed solution demonstrates its effectiveness and potential for real-world applications, offering an accessible and powerful tool for promoting a safe and respectful online environment.