Paper Title
A COMPREHENSIVE ANALYSIS OF RECENT ADVANCEMENTS IN DEEP LEARNING BASED CYBERBULLYING TEXT DETECTION

Abstract
The rise of digital technology in the modern era and the proliferation of online social media platforms and different online forums have led to unparalleled degrees of communication and sharing of information. Amidst the benefits, it has also led to a pervasive issue in the digital world known as cyberbullying, leading to significant challenges to the well-being of individuals and a threat to societal harmony. This paper presents a thorough review of the recent advancements in deep learning techniques by various researchers to automate the process of cyberbullying detection. This paper investigates various deep learning techniques like Convolutional neural networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), transformers, Graph Convolution Networks (GCN), and hybrid approaches.Thesurvey is conducted on various private and publicly available datasets to gain more insights about these diverse Deep Learning techniques, highlighting their performance, strengths, and limitations. The experiment reveals that LSTM andBi-LSTM deep models achieved exceptional performance and BERT, m-BERT and modified BERT models have achieved good F-1 scores in detecting toxic content across multiple languages. The hybrid-based models and the introduction of the GCN model are also showing promising results in this domain. This comprehensive in-depth analysiswill provide valuable insights for researchers, practitioners, and policymakers that will guide in combating cyberbullying detection and the selection and choice of appropriate models. Keywords - Cyber Hate Speech,BiGRU, DNN,GCN, OSN.