Abstract
The harmful effects of cyberbullying, including mental health problems, poor academic performance, and suicidal thoughts, highlight the importance of developing effective detection systems. This text discusses various approaches to detecting and combating cyberbullying on social media platforms. A cyberbullying detection system (CDS) could identify different types of bullying based on gender, religion, ethnicity, age, aggression, and non-cyberbullying. The CDS system utilized a hybrid deep learning architecture that integrated convolutional neural networks (CNN) with bidirectional long short-term memory networks (BiLSTM). Both binary and multiclass classification datasets were used, and the BiLSTM outperformed the combined CNN-BiLSTM classifier in detecting online bullying with an accuracy rate of 99%. A novel algorithm called CNN-CB was proposed to eliminate the need for feature engineering and to produce better predictions than traditional cyberbullying detection approaches. CNN-CB utilizes convolutional neural networks and incorporates semantics through the use of word embedding. Experiments showed that CNN-CB outperformed traditional content-based cyberbullying detection with an accuracy of 95%.
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