Abstract
Cyberbullying is characterized by deliberate and sustained peer aggression, as well as a power differential between the victim and the perpetrators or abusers. Cyberbullying can have a variety of consequences for victims, including mental health problems, poor academic performance, a tendency to drop out of work, and even suicidal thoughts. The main objective of this study was to develop a cyberbullying detection system (CDS) to uncover hateful and abusive behaviour on social media platforms. Two experiments were carried out to train and test the proposed system with binary and multiclass cyberbullying classification datasets. Hybrid deep learning architecture consisting of convolutional neural networks integrated with bidirectional long short-term memory networks (CNN-BiLSTM) and single BiLSTM models were compared in terms of their ability to classify social media posts into several bullying types related to gender, religion, ethnicity, age, aggression, and non-cyberbullying. Both classifiers showed promising performance in the binary classification dataset (aggressive or non-aggressive bullying), with a detection accuracy of 94%. For the multiclass dataset, BiLSTM outperformed the combined CNN-BiLSTM classifier, achieving an accuracy of 99%. A comparison of our method to the existing method on the multiclass classification dataset revealed that our method performed better in detecting online bullying.