  Harassment by cyberbullies is a significant phenomenon on the social media.
Existing works for cyberbullying detection have at least one of the following
three bottlenecks. First, they target only one particular social media platform
(SMP). Second, they address just one topic of cyberbullying. Third, they rely
on carefully handcrafted features of the data. We show that deep learning based
models can overcome all three bottlenecks. Knowledge learned by these models on
one dataset can be transferred to other datasets. We performed extensive
experiments using three real-world datasets: Formspring (12k posts), Twitter
(16k posts), and Wikipedia(100k posts). Our experiments provide several useful
insights about cyberbullying detection. To the best of our knowledge, this is
the first work that systematically analyzes cyberbullying detection on various
topics across multiple SMPs using deep learning based models and transfer
learning.
