  As research on hate speech becomes more and more relevant every day, most of
it is still focused on hate speech detection. By attempting to replicate a hate
speech detection experiment performed on an existing Twitter corpus annotated
for hate speech, we highlight some issues that arise from doing research in the
field of hate speech, which is essentially still in its infancy. We take a
critical look at the training corpus in order to understand its biases, while
also using it to venture beyond hate speech detection and investigate whether
it can be used to shed light on other facets of research, such as popularity of
hate tweets.
