Arabic Cyberbullying Detection Using Support Vector Machine with Cuckoo Search

Abstract

      Cyberbullying is one of the biggest electronic problems that takes multiple forms of harassment using various social media. Currently, this phenomenon has become very common and is increasing, especially for young people and adolescents. Negative comments have a significant and dangerous impact on society in general and on adolescents in particular. Therefore, one of the most successful prevention methods is to detect and block harmful messages and comments. In this research, negative Arabic comments that refer to cyberbullying will be detected using a support vector machine algorithm. The term frequency-inverse document frequency vectorizer and the count vectorizer methods were used for feature extraction, and the results were improved using the cuckoo search algorithm. The resulting accuracy before and after optimizing the support vector machine’s hyperparameters is 85.8% and 87.1%, respectively.