Journal of Information Technology and Policy Examining the weak and strong Islamophobic hate speech on social media

Islamophobic hate speech on social media is a growing concern in contemporary Western politics
and society. It can inflict considerable harm on any victims who are targeted, create a sense of fear
and exclusion amongst their communities, toxify public discourse and motivate other forms of
extremist and hateful behavior. Accordingly, there is a pressing need for automated tools to
detect and classify Islamophobic hate speech robustly and at scale, thereby enabling quantitative
analyses of large textual datasets, such as those collected from social media. Previous research has
mostly approached the automated detection of hate speech as a binary task. However, the varied
nature of Islamophobia means that this is often inappropriate for both theoretically informed
social science and effective monitoring of social media platforms. Drawing on in-depth conceptual
work we build an automated software tool which distinguishes between non-Islamophobic, weak
Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%.
Our tool enables future quantitative research into the drivers, spread, prevalence and effects of
Islamophobic hate speech on social media.