CBDC-Net: Recurrent Bidirectional LSTM Neural Networks Based Cyberbullying Detection with Synonym-Level N-Gram and TSR-SCSOFeatures

Social networks Cyber bullying has become another common problem in online social networks (OSNs) which exposes individuals to high risks of their mental health and interacting with others. Previous work in cyber bullying detection is often confronted with limitations in accurately detecting abusive behavior because of the intricacies in cyber space and evolution of cyber bullying practices. A new approach of Cyber bullying detection and classification network (CBDC- Net) for improving the effectiveness of detection of cyber bullying in OSNs based on natural language processing features, feature selection techniques, and deep learning algorithms is also presented in this study. CBDC-Net can overcome these challenges to existing detection methods of cyber bullying using innovative Natural Language Processing (NLP) and Deep Learning approaches. In the data preprocessing step, CBDC-Net filter and normalize the text data that is openly collected from OSNs. After that, CBDC-Net extracts features using a Synonym Level N-Gram (SLNG) approach and it incorporates both the word and character-based information to make the synonyms of text much better than the other method. After that, CSI of CBDC-Net applied Textual Similarity Resilient Sand Cat Swarm Optimization (TSR-SCSO) for feature selection to give an iterative value of their features’ importance level to detect cyber bullying. Last, in CBDC-Net, a Recurrent Bidirectional Long Short-Term Memory (LSTM)Neural Network for classification (RBLNN) is used as classification approach is applied, which recognizes the sequential nature of textual data enabling proper distinction between cyber bullying cases. Last but not the least, the CBDC Net provides a promising solution for solving the mentioned problems of cyber bullying detection in OSNs.