Cyber Aggression and Cyberbullying Identification on Social Networks

Abstract

Bullying includes aggression, harassment, and discrimination. The phenomenon has widespread with the great diffusion of many social networks. Thus, the cyber aggression iteration turns into a more serious problem called Cyberbullying. In this work an automatic identification system built up on the most performing set of techniques available in literature is presented. Textual comments of various Italian Twitter posts have been processed to identify the aggressive phenomenon. The challenge has been also identifying aggressive profiles who repeat their malicious work on social networks. Two different experiments have been performed with the aim of the detection of Cyber Aggression and Cyberbullying. The best results were obtained by the Random Forest classifier, trained on an ad-hoc Dataset that contemplates a series of comments extracted from Twitter and tagged manually. The system currently presented is an excellent tool to counter the phenomenon of Cyberbullying, but there are certainly many improvements to be made to improve the performance of the system.