Next-Gen Cyberbullying Prediction with NLP and Recurrent Neural Networks

Cyberbullying still submerges itself in online communities, for which there are effective models for prediction and intervention against bad actions. This research presents the next generation model, predicting cyberbullying incidents in the text data using Natural Language Processing (NLP) and Recurrent Neural Networks (RNNs). As different semantics and context cues appear in online communications, it aims at high-class counting of messages either under bullying or non-bullying categories. Key aspects involve the use of word embedding's for semantic representation, sequential learning using RNNs for capturing temporal relationships in text, and ensemble methods for resilient model performance. Evaluation metrics show the effectiveness of the model in identifying cyberbullying behaviors, thus allowing timely interventions.