  The pervasiveness of the Internet and social media have enabled the rapid and
anonymous spread of Hate Speech content on microblogging platforms such as
Twitter. Current EU and US legislation against hateful language, in conjunction
with the large amount of data produced in these platforms has led to automatic
tools being a necessary component of the Hate Speech detection task and
pipeline. In this study, we examine the performance of several, diverse text
representation techniques paired with multiple classification algorithms, on
the automatic Hate Speech detection and abusive language discrimination task.
We perform an experimental evaluation on binary and multiclass datasets, paired
with significance testing. Our results show that simple hate-keyword frequency
features (BoW) work best, followed by pre-trained word embeddings (GLoVe) as
well as N-gram graphs (NGGs): a graph-based representation which proved to
produce efficient, very low-dimensional but rich features for this task. A
combination of these representations paired with Logistic Regression or 3-layer
neural network classifiers achieved the best detection performance, in terms of
micro and macro F-measure.
