Adopting MaxEnt to Identification of Bullying Incidents in Social Networks

Bullying is a widespread problem in cyberspace and social networks. Therefore, in the recent years many studies have been dedicated to cyberbullying. Lack of appropriate dataset, due to variety of reasons, is one of the major obstacles faced in most studies. In this work we suggest that to overcome some of these barriers a model should be employed which is minimally affected by prevalence and small sample size. To this end we adopted the use of the Maximum Entropy method (MaxEnt) to identify the bully users in YouTube. The final results were compared with the commonly used methods. All models provided reasonable prediction of the bullying incidents. MaxEnt models had the highest discrimination capacity of bullying posts and the lowest sensitivity towards prevalence. We demonstrate that MaxEnt can be successfully adopted to cyberbullying studies with imbalanced datasets.