One from of online misbehaviour which has deeply affected society with harmful consequences is known as cyberbullying. Cyberbullying can simply be defined as an intentional act that is conducted through digital technology to hurt someone. Cyberbullying is a widely covered topic in the social sciences. However, studies on the technical dimensions of this topic are relatively rare. In this research the overall goal was to bridge the gap between social science approaches and technical solutions have thoroughly studied the origin of cyberbullying and its growth over time, as well as the role of technology in the emergence of this type of virtual behaviour and in the potential for reducing the extent of the social concern it raises.
This thesis introduces a novel outlook towards the cyberbullying phenomenon. It looks into the gradual changes which have occurred in relationships and social communication with the emergence of the Internet. This study leads to the conviction that for combating cyberbullying, behavioural and psychological studies, and the study of technical solutions should go hand in hand. It was also shown that besides the conventional features used for text mining methods more personal features, such as gender and age of the users, can improve the accuracy of the detection models. We also study the use of experts' knowledge and human reasoning to identify potential bully users in social networks. A hybrid approach is designed, incorporating machine learning models on top of the expert system, which outperforms each of the models individually.
The integration of social studies into a software-enhanced monitoring workflow could pave the way towards the tackling of this kind of online misbehaviour. This work can be viewed as a contribution to the more general societal challenge of increasing the level of cybersecurity, in particular for the younger generations of social network users.