Taxonomy of Cyberbullying Detection and Prediction Techniques in Online Social Networks

Abstract

Online social networking sites have become very popular in this era due to easy accessibility of Internet. This popularity leads to continuous availability of multiple users, which resultantly attract more criminals and hence increasing insecurity in OSN. Different types of crimes are committed for multiple reasons in cyber realm by taking assistance of cyber technology. This insecure environment of OSN needs attention to prevent the damage caused by these crimes to society. Cyberbullying is reported as one of the harmful crimes causing psychological damage to victims. Cyberbullying has dangerous effects on the victim, which may also lead the victim to suicidal attempt. Victims of cyberbullying are usually afraid or embarrassed to reveal about their harassment. It has become a necessity to detect and prevent cyberbullying. Many researchers are working in multiple directions to achieve best results for automated cyberbullying detection. We have done a broad survey of all recent techniques proposed by researchers for cyberbullying detection and prediction. In the paper, we have presented taxonomy of multiple methods being used for cyberbullying detection. We also have presented a comparative analysis and classification of the work done in recent years.