Detecting multimodal cyber-bullying behaviour in social-media using deep learning techniques

Cyberbullying detection refers to the process of classifying and identifying of cyberbullying behavior—which involves the use of technology to harass, or bullying individuals, typically through online platforms. A growing concern is the spread of bullying memes on social media, which can perpetuate harmful behavior. While much of the existing research focuses on detecting cyberbullying in text-based data, image-based cyberbullying has not received as much attention. This is a significant issue because many social media posts combine images with text, and the visual content can be a key component of cyberbullying. To address this, our research aims to develop a multimodal cyberbullying detection modal (MCB) that is capable of detecting bullying in both images and text. For this, we used VGG16 pretrained model to detect bullying in images and XLM-RoBERTa with BiGRU model to detect bullying in text. Together we integrated these models (VGG16 + XLM-RoBERTa and BiGRU) using attention mechanisms, CLIP, feedback mechanisms, CentralNet, etc., and proposed a model used for detecting cyberbullying in image-text-based memes. Our accomplished model produced a reasonable accuracy of 74%, pointing that the system is effective in recognizing most cyberbullying activity.