A Comparison Study of Cyberbullying Detection Using Various Machine Learning Algorithms

Cyberbullying is one of the most notable current social media issues. Cyberbullying is a type of bullying or harassment that occurs online. The prevalence of cyberbullying is growing as the digital age and technology advances. Cyber-threats are now a significant problem that affects students, particularly teenagers. The victims of bullying may experience negative effects on their emotional health. Additionally, awareness has grown because of some suicide incidents. Bullying occurs most frequently on networks with direct messaging. The availability of tools that can automatically detect potential behaviors categorized as cyberbullying can be useful. In this paper, we have compared the accuracy results of various machine learning algorithms for detecting, identifying, and classifying the cyberbullying texts or sentences on various social media sites like Twitter that could possibly lead to a cyberbullying episode.