  Islamophobic hate speech on social media inflicts considerable harm on both
targeted individuals and wider society, and also risks reputational damage for
the host platforms. Accordingly, there is a pressing need for robust tools to
detect and classify Islamophobic hate speech at scale. Previous research has
largely approached the detection of Islamophobic hate speech on social media as
a binary task. However, the varied nature of Islamophobia means that this is
often inappropriate for both theoretically-informed social science and
effectively monitoring social media. Drawing on in-depth conceptual work we
build a multi-class classifier which distinguishes between non-Islamophobic,
weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and
balanced accuracy is 83%. We apply the classifier to a dataset of 109,488
tweets produced by far right Twitter accounts during 2017. Whilst most tweets
are not Islamophobic, weak Islamophobia is considerably more prevalent (36,963
tweets) than strong (14,895 tweets). Our main input feature is a gloVe word
embeddings model trained on a newly collected corpus of 140 million tweets. It
outperforms a generic word embeddings model by 5.9 percentage points,
demonstrating the importan4ce of context. Unexpectedly, we also find that a
one-against-one multi class SVM outperforms a deep learning algorithm.
