A hybrid approach for cyberbullying detection

Cyberbullying has been a type of oppression that has become widespread in the world since the early 2000s and has become one of the major problems of modern society over the years. The widespread use of social media has led to an increase in cyberbullying data. As the findings increased, the amount of data increased and automated methods for cyberbullying detection began to be sought. In the literature, studies have been carried out to detect cyberbullying with many methods such as machine learning, natural language processing, deep learning and feature selection. This study reveals that the use of the Genetic Algorithm (GA) which has achieved great success in solving many problems in computer science in recent years, and the Whale Optimization Algorithm (WOA) which is one of the swarm optimization algorithms inspired by nature, in a hybrid way in feature selection can be an effective method in the classification of cyberbullying data. The Genetic Whale Optimization (GWOA) presented in the study gave better results in accuracy and F1-Score than GA and WOA alone.