Cyberbullying Detection in Social Networks: A Multi-Stage Approach

Abstract

Cyberbullying, defined as the violent harassment of an individual towards a victim in electronic media, is a serious problem nowadays. If not prevented or mitigated, it can lead to affective disorders, poor academic performance, problems in social relationships, and-ultimately-to suicide attempts in youngsters and children. Because manual supervision in social networks (a space where cyberbullying can naturally occur) is laborious, automated approaches for cyberbullying detection are desirable. However, a considerable number of approaches treat this problem as merely aggressive text message detection, without considering the frequency of the harassment. The approach proposed in this work, in contrast, views cyberbullying detection as a process of three consecutive stages: aggressive text message detection, alleged aggressor and victim detection, and cyberbullying case detection. This approach was tested using a dataset extracted from Twitter; the approach obtained an F-score of 0.947 for the positive (cyberbullying) case.