  Cyberbullying is a problem in today's ubiquitous online communities.
Filtering it out of online conversations has proven a challenge, and efforts
have led to the creation of many different datasets, all offered as resources
to train classifiers. Through these datasets, we will explore the variety of
definitions of cyberbullying behaviors and the impact of these differences on
the portability of one classifier to another community. By analyzing the
similarities between datasets, we also gain insight on the generalization power
of the classifiers trained from them. A study of ensemble models combining
these classifiers will help us understand how they interact with each other.
