Cyberbully Detection Using Intelligent Techniques
J.I. Sheeba, S. Pradeep Devaneyan · International Journal Of Data Mining And Emerging Technologies · 2016
Cyberbullying is a rising anxiety in online communications. It is socially aggressive and powerfully affects individuals, specifically adolescents and youngsters. Sending or posting harmful, cruel text and images to insult or threaten a victim through online social networks. As a result of the invention of friendships, social networks, social communication and relationships, one may have thousands of ‘friends’ without seeing faces. It is necessary to detect the cyberbully words and give warning to the innocent children and adolescents, those who use social networks. Cyberbullying detection has mainly focused on the content of the conversations, whereas largely ignoring the individuality of the actors concerned in cyberbullying. This framework is proposed to detect the cyberbully content present in the social network using GenLeven algorithm and to classify the detected cyberbully content as harassment cyberbully, insult cyberbully, terrorism cyberbully or flaming cyberbully using fuzzy rule base.