  Cyberbullying is a disturbing online misbehaviour with troubling
consequences. It appears in different forms, and in most of the social
networks, it is in textual format. Automatic detection of such incidents
requires intelligent systems. Most of the existing studies have approached this
problem with conventional machine learning models and the majority of the
developed models in these studies are adaptable to a single social network at a
time. In recent studies, deep learning based models have found their way in the
detection of cyberbullying incidents, claiming that they can overcome the
limitations of the conventional models, and improve the detection performance.
In this paper, we investigate the findings of a recent literature in this
regard. We successfully reproduced the findings of this literature and
validated their findings using the same datasets, namely Wikipedia, Twitter,
and Formspring, used by the authors. Then we expanded our work by applying the
developed methods on a new YouTube dataset (~54k posts by ~4k users) and
investigated the performance of the models in new social media platforms. We
also transferred and evaluated the performance of the models trained on one
platform to another platform. Our findings show that the deep learning based
models outperform the machine learning models previously applied to the same
YouTube dataset. We believe that the deep learning based models can also
benefit from integrating other sources of information and looking into the
impact of profile information of the users in social networks.
