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This document sets up the main recommendations identified through the three years of engagement and dialouge on this topic. It is anchored in the discussions from the round tables. It further builds on the experiences from UN field presences that are working to address these challenges at country level. The paper is the result of a cooperation between my Office and the ESRC Human Rights, Big Data and Technology Project at the University of Essex. I would like to especially thank Ahmed Shaheed, Deputy Director, Essex Human Rights, Big Data and Technology Project and former United Nations Special Rapporteur on Freedom of Religion or Belief for this partnership over the last several years and which has resulted in this policy paper.

This study investigates the evolving challenges intelligence and law enforcement agencies face in countering Salafi jihadist terrorist activities across digital platforms, focusing on integrating artificial intelligence (AI) and data fusion technologies in open-source intelligence (OSINT) methodologies. Through an analysis of case studies involving ISIS and other non-state actors, the study examines how terrorist organisations adapt their digital strategies to evade detection while exploiting social media platforms, instant messaging applications and websites for recruitment, propaganda, and operational planning. The study demonstrates the necessity of developing advanced technological solutions such as the integration of AI-enhanced OSINT capabilities, combined with data fusion approaches, that can improve analysts’ ability to process multilingual content, identify threat actors who use online anonymity, and detect patterns of terrorist activity across multiple network layers. However, the study also reveals critical ethical and operational challenges in balancing effective counter-terrorism measures with privacy rights and civil liberties. The study concludes that while OSINT, AI and data fusion tools can substantially augment human analysis capabilities, successful counter-terrorism efforts require a carefully calibrated approach combining technological innovation and human expertise.

Recent years have seen a substantial increase in far-right inspired attacks. In this context, the present article offers an analytical framework for the study of right-wing extremists’ multifaceted and fast-growing activity on the Internet. Specifically, we conceptualize the far-right online presence as a dynamic ecosystem, teasing out four major components that correspond to the different levels of analysis available to future research. We illustrate the benefits of this framework with key illustrative examples from the English-, French-, and German- speaking far-right, revealing the worrying size and breadth – but also heterogeneity – of today’s far-right online ecosystem.

Recent years have seen increased interest in the online presence of extreme right groups. Although originally composed of dedicated websites, the online extreme right milieu now spans multiple networks, including popular social media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any contemporary analysis of online extreme right activity requires the consideration of multiple data sources, rather than being restricted to a single platform. We investigate the potential for Twitter to act as one possible gateway to communities within the wider online network of the extreme right, given its facility for the dissemination of content. A strategy for representing heterogeneous network data with a single homogeneous network for the purpose of community detection is presented, where these inherently dynamic communities are tracked over time. We use this strategy to discover and analyse persistent English and German language extreme right communities.

Two notable features of the current conflict in Syria and Iraq are the number of foreign fighters from western countries fighting for Sunni militant organisations, and the use of the Internet and social media by some extremist groups to disseminate propaganda material. This article explores how the group which refers to itself as Islamic State and an affiliated British group, Rayat al Tawheed, deploy combinations of images and text which serve as bonding icons to rally supporters. The data consists of the English language edition of ISIS’s online magazine Dabiq and online materials produced by Rayat al Tawheed. The results suggest that ISIS and Rayat al Tawheed adopt similar but different iconisation strategies. While ISIS adopts a global strategy to present a unified world view utilising a range of ISIS values in its iconisation, Rayat al Tawheed foregrounds jihad using strategies specifically targeting young, English-speaking men of Islamic/Arab backgrounds.

As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and we discuss its implications for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.