  White supremacists embrace a radical ideology that considers white people
superior to people of other races. The critical influence of these groups is no
longer limited to social media; they also have a significant effect on society
in many ways by promoting racial hatred and violence. White supremacist hate
speech is one of the most recently observed harmful content on social
media.Traditional channels of reporting hate speech have proved inadequate due
to the tremendous explosion of information, and therefore, it is necessary to
find an automatic way to detect such speech in a timely manner. This research
investigates the viability of automatically detecting white supremacist hate
speech on Twitter by using deep learning and natural language processing
techniques. Through our experiments, we used two approaches, the first approach
is by using domain-specific embeddings which are extracted from white
supremacist corpus in order to catch the meaning of this white supremacist
slang with bidirectional Long Short-Term Memory (LSTM) deep learning model,
this approach reached a 0.74890 F1-score. The second approach is by using the
one of the most recent language model which is BERT, BERT model provides the
state of the art of most NLP tasks. It reached to a 0.79605 F1-score. Both
approaches are tested on a balanced dataset given that our experiments were
based on textual data only. The dataset was combined from dataset created from
Twitter and a Stormfront dataset compiled from that white supremacist forum.
