Deep Learning Approaches for Detecting Arabic Cyberbullying Social Media

The widespread use of social media has escalated concerns about cyberbullying. Traditional methods for detecting and managing cyberbullying struggle with the sheer volume of electronic text data. This has led to the exploration of deep learning as a potential solution. Researchers study focuses on implementing deep learning techniques to identify cyberbullying in Arabic social media, specifically targeting three prevalent forms of Arabic: dialectal, Modern Standard, and Classical. The collected data corpus was about 30, 0000 tweets. In this work, we first examined the sentiment analysis as cyberbullying, and No cyberbullying, then we further classified the cyberbullying by labelling the data under six different cyberbullying categories. We implemented deep learning models such as CNN, RNN, and a combination of CNN-RNN. The results that obtained from 2-classes classification showed a superiority of LSTM in terms of accuracy with 95.59%, while the best accuracy in the 6-classes classification gained from implementing CNN with 78.75%. Meanwhile the f1-score results were the highest in LSTM for the 2-lasses and 6-classes classifications with 96.73%, and 89%, respectively. These findings emphasize the potential for deep learning techniques to be applied in the development of automated systems for identifying and combating cyberbullying on social media and show how well they work in detecting cyberbullying.