Detection and Classification of Cyberbullying Using CR*
R Gayathri, Lydia Beryl D, M Gowtham, Naveen Kumar N, M. S. Anbarasi · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: Cyberbullying is one of the latest threats in the online world, affecting millions of people worldwide. The detection of cyberbullying became a challenging task due to the complexity involved. In this paper, we propose a novel approach for cyberbullying detection using CR* which concatenates the deep learning model’s features such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) which helps in detecting text, audio, and emoji. Initially, we collect a dataset of cyberbullying messages from social media platforms. Then we integrate convolutional neural networks and recurrent neural networks to build our deep learning model entitled Convolutional Recurrent (CR*) to extract features from a combination of the text data, emoji, and audio. The multimodal features were then concatenated and passed through fully connected layers for classification. The proposed approach can be useful for detecting cyberbullying on various online platforms and can help prevent the spread of cyberbullying.