Islamophobes are not all the same! A study of far right actors on Twitter

TL;DR

This study analyzes five million tweets over a year to identify and categorize different types of Islamophobic far right actors on Twitter, revealing that a small number of users generate most hate speech and discussing policy implications.

Contribution

It introduces a new typology of far right Islamophobic actors on Twitter based on large-scale data analysis, combining machine learning and latent Markov models.

Findings

Seven types of Islamophobic actors identified

Most Islamophobia is produced by a small subset of users

Temporal and behavioral differences among actor types

Abstract

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.