  Far-right actors are often purveyors of Islamophobic hate speech online,
using social media to spread divisive and prejudiced messages which can stir up
intergroup tensions and conflict. Hateful content can inflict harm on targeted
victims, create a sense of fear amongst communities and stir up intergroup
tensions and conflict. Accordingly, there is a pressing need to better
understand at a granular level how Islamophobia manifests online and who
produces it. We investigate the dynamics of Islamophobia amongst followers of a
prominent UK far right political party on Twitter, the British National Party.
Analysing a new data set of five million tweets, collected over a period of one
year, using a machine learning classifier and latent Markov modelling, we
identify seven types of Islamophobic far right actors, capturing qualitative,
quantitative and temporal differences in their behaviour. Notably, we show that
a small number of users are responsible for most of the Islamophobia that we
observe. We then discuss the policy implications of this typology in the
context of social media regulation.
