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From a technical standpoint, Islamic State (IS) videos are demonstrably superior to those of other groups. But as time goes by, their aesthetic is migrating downstream as other groups attempt to copy it. Specifically, IS has turned to video games, regularly mimicking and even directly copying the aesthetic and design of First Person Shooter games, most often Call of Duty, in their videos, and other groups have followed suit. This specific aesthetic offers a way to recruit young, technologically savvy, men while sanitizing the violence they were being recruited to participate in. This study offers an instrument for tracking the IS aesthetic as it moves to other groups as well as its evolution over time, and offers a case study of a specific group that has copied the IS aesthetic, Hay’at Tahrir al-Sham (HTS.)

This briefing is the first in ISD’s Gaming and Extremism Series exploring the role online gaming plays in the strategy of far-right extremists in the UK and around the world. This is part of a broader programme on the “Future of Extremism” being delivered by ISD in the second half of 2021, charting the transformational shifts in the extremist threat landscape two decades since 9/11, and the policy strategies required to counter the next generation of extremist threats. The series will include reports exploring extremist activity on four gaming-related platforms: Steam, Discord, DLive and Twitch.

This report maps the ecosystem of the burgeoning
‘new’ extreme right across Europe and the US, which is
characterised by its international outlook, technological
sophistication, and overtures to groups outside of the
traditional recruitment pool for the extreme-right. This
movement is marked by its opportunistic pragmatism,
seeing movements which hold seemingly contradictory
ideologies share a bed for the sake of achieving
common goals. It examines points of connectivity
and collaboration between disparate groups and
assesses the interplay between different extreme-right
movements, key influencers and subcultures both
online and offline.

This report outlines the results of a programme trialling
a methodology for identifying individuals who are
demonstrating signs of radicalisation on social media,
and engaging these individuals in direct, personalised
and private ‘counter-conversations’ for the purpose of
de-radicalisation from extremist ideology and
disengagement from extremist movements. This is
the first programme globally which has trialled the
delivery of online interventions in a systematised
and scaled fashion.

This report represents the interim findings of a two-year study designed to increase understanding of the social media footprint of right-wing extremism (RWE) in Canada. This work is part of a larger project designed to understand RWE in Canada led by Ontario Tech University (OTU), in partnership with Michigan State University and the University of New Brunswick. This team are currently working on a similar project designed to map offline RWE in Canada. The project follows a similar study delivered in 2015, enabling researchers and policymakers to understand how RWE has changed in the past five years.

A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. We used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords. We use crowd-sourcing to label a sample of these tweets into three categories: those containing hate speech, only offensive language, and those with neither. We train a multi-class classifier to distinguish between these different categories. Close analysis of the predictions and the errors shows when we can
reliably separate hate speech from other offensive language and when this differentiation is more difficult. We find that racist and homophobic tweets are more likely to be classified as hate speech but that sexist tweets are generally classified as offensive. Tweets without explicit hate keywords are also more difficult to classify.