The idea that “hate speech” is something that can be objectively identified is taken for granted on the Left, which is increasingly open about its authoritarian desire to destroy the freedom of speech and regulate what speech is acceptable in the public square and what speech is not. Honest analysis of the motivating ideology behind jihad terror has been stigmatized for years as “Islamophobic hate speech,” despite the fact that it is absolutely nothing like the examples of “Islamophobic hate speech” given in this article. When Bertie Vidgen and Taha Yasseri succeed in silencing that analysis, the jihad will be able to advance unopposed and unimpeded. But hey, as Britain collapses into chaos, at least there will be no “Islamophobic hate speech.”

“How we built a tool that detects the strength of Islamophobic hate speech on Twitter,” by Bertie Vidgen and Taha Yasseri, The Conversation, January 2, 2019:

In a landmark move, a group of MPs recently published a working definition of the term Islamophobia. They defined it as “rooted in racism”, and as “a type of racism that targets expressions of Muslimness or perceived Muslimness”.

In our latest working paper, we wanted to better understand the prevalence and severity of such Islamophobic hate speech on social media. Such speech harms targeted victims, creates a sense of fear among Muslim communities, and contravenes fundamental principles of fairness. But we faced a key challenge: while extremely harmful, Islamophobic hate speech is actually quite rare.

Billions of posts are sent on social media every day, and only a very small number of them contain any sort of hate. So we set about creating a classification tool using machine learning which automatically detects whether or not tweets contain Islamophobia.

Detecting Islamophobic hate speech

Huge strides have been made in using machine learning to classify more general hate speech robustly, at scale and in a timely manner. In particular, a lot of progress has been made to categorise content based on whether it is hateful or not.

But Islamophobic hate speech is much more nuanced and complex than this. It runs the gamut from verbally attacking, abusing and insulting Muslims to ignoring them; from highlighting how they are perceived to be “different” to suggesting they are not legitimate members of society; from aggression to dismissal. We wanted to take this nuance into account with our tool so that we could categorise whether or not content is Islamophobic and whether the Islamophobia is strong or weak.

We defined Islamophobic hate speech as “any content which is produced or shared which expresses indiscriminate negativity against Islam or Muslims”. This differs from but is well-aligned with MPs’ working definition of Islamophobia, outlined above. Under our definitions, strong Islamophobia includes statements such as “all Muslims are barbarians”, while weak Islamophobia includes more subtle expressions, such as “Muslims eat such strange food”.

Being able to distinguish between weak and strong Islamophobia will not only help us to better detect and remove hate, but also to understand the dynamics of Islamophobia, investigate radicalisation processes where a person becomes progressively more Islamophobic, and provide better support to victims….

Detecting Islamophobic hate speech is a real and pressing challenge for governments, tech companies and academics. Sadly, this is a problem that will not go away – and there are no simple solutions. But if we are serious about removing hate speech and extremism from online spaces, and making social media platforms safe for all who use them, then we need to start with the appropriate tools. Our work shows it’s entirely possible to make these tools – to not only automatically detect hateful content but to also do so in a nuanced and fine-grained manner.