BERT-Based Cyberbullying Detection

Abstract

Cyberbullying on social media has been on the rise lately-and with it, the serious psychological effects it leaves in its wake: anxiety and sadness. That's why early detection and intervention are so crucial. Those traditional methods of tackling online abuse often fall short when it comes to slang and ever-changing language. That's because they just can't pick up on the intent behind the words. Our project tackles that problem head-on by combining text and visual elements in a way that deep learning can really understand. We use a refined BERT model to put language in context, Demoji to decipher the meanings of emojis and Pytesseract to extract text from images. That hybrid approach ensures even the most hidden or indirect bullying messages are identified. We deliver that analysis-and the tools to visualize it-in real-time through a mobile app. That means non-technical users-parents, teachers and moderators-can easily use it to spot and stop cyberbullying. By harnessing the latest AI technologies to safeguard vulnerable people, we create a safer online environment.