  Automated hate speech detection is an important tool in combating the spread
of hate speech, particularly in social media. Numerous methods have been
developed for the task, including a recent proliferation of deep-learning based
approaches. A variety of datasets have also been developed, exemplifying
various manifestations of the hate-speech detection problem. We present here a
large-scale empirical comparison of deep and shallow hate-speech detection
methods, mediated through the three most commonly used datasets. Our goal is to
illuminate progress in the area, and identify strengths and weaknesses in the
current state-of-the-art. We particularly focus our analysis on measures of
practical performance, including detection accuracy, computational efficiency,
capability in using pre-trained models, and domain generalization. In doing so
we aim to provide guidance as to the use of hate-speech detection in practice,
quantify the state-of-the-art, and identify future research directions. Code
and dataset are available at
https://github.com/jmjmalik22/Hate-Speech-Detection.
