Ontology and AI Integration for Real-Time Detection of Cyberbullying Among University Students

Abstract

With the increasing of the internet, smartphones, and social media, nearly everyone is a potential target for cyberbullying. Our research introduces an AI-driven approach to detect and address cyberbullying among college students, with a focus on its impact on mental health. We developed a context-specific ontology, drawing from real-time data, publicly available data, surveys, academic literature, and social media interactions to categorize information into domains such as victims, causes, types, environments, impacts, and responses. We collected real-time data from college students through surveys, interviews, and social media, leveraging advanced NLP (Natural Language Processing) techniques and BERT for accurate and efficient detection. By integrating this ontology with AI, our system dynamically adapts to emerging cyberbullying patterns, offering more precise detection and response strategies. Experimental results show that the proposed model achieves 96.2% accuracy, with 95.8% precision, 95.5% recall, and an F1-score of 95.6%. This performance surpasses traditional methods, emphasizing its capability to identify both explicit and implicit forms of abusive behavior. The approach not only introduces a tailored ontology for college students' unique social dynamics but also offers solutions to evolving cyberbullying trends. This research significantly enhances online safety and fosters a healthier digital environment for university students use.