A Survey on Cyberbullying Predictive Model using Deep Learning Techniques

Abstract

Cyberbullying, a pervasive issue in the current digital age, has prompted the need for advanced predictive models to identify and mitigate online harassment. This survey paper explores the landscape of cyberbullying severity level prediction using an ensemble-based deep learning approach for multimodal datasets. Delving into the realm of deep learning techniques and investigating their efficacy in discerning subtle patterns indicative of cyberbullying behaviour, the survey encompasses a comprehensive review of existing ensemble methodologies, highlighting their strengths and weaknesses in cyberbullying prediction. Diverse datasets, model architectures, and evaluation metrics employed in relevant studies are analysed, aiming to provide a thorough understanding of the current technological status. Additionally, difficulties and potential avenues for upcoming studies are discussed, fostering advancements in the development of robust predictive models to combat cyberbullying effectively. Researchers, practitioners, and policymakers looking for insights on the changing field of cyberbullying prevention using ensemble-based deep-learning methodologies will find this survey to be a valuable resource.