Cyberbullying Detection in Heterogeneous Data Streams Using Pytesseract OCR and HateBERT: A Cross-Modal Approach for Text, Image, and Emoji Interpretation

Abstract: With the exponential growth of social media platforms, cyberbullying has become a pressing issue affecting users worldwide. This paper presents a Smart Cyberbullying Detection and Intervention System that leverages advanced AI models to identify and address harmful online behavior. The proposed system employs the HateBERT model for robust detection of toxic content, processing both textual inputs (such as comments and tweets) and multimodal content like memes that combine images and text. A multilingual approach ensures inclusivity across diverse user bases, allowing for effective detection in various languages. In addition to detecting the presence of cyberbullying, it classifies the type of bullying (e.g., age, racism, gender, ethnicity), enabling more nuanced responses. Additionally, the system integrates a chatbot-based counseling module to provide real-time, empathetic support to affected users. Built with a scalable architecture, the solution includes components for content analysis, user interaction, and mental health support. This comprehensive framework not only enhances the accuracy of cyberbullying detection across media types but also offers immediate, human-like intervention, promoting safer and more supportive online communities.