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This paper explores the uses of YouTube by Uyghur nationalist movement activists and studies various ideological codes used by different communities to promote their messages. It argues that several ideological codes are produced in order to challenge the dominant ideologies promoted by the Chinese government, which create a ground for Uyghur ‘imagined solidarity’ across physical borders. Analysis of the production of audio-visual messages by the dispersed ethnic group provides an important window into how ethnic identity is forged by means of Web 2.0.

VOX-Pol Blog Post.

VOX-Pol Blog post.

Researchers and policymakers have supported a public health approach to countering violent extremism throughout the War on Terror. However, barriers to obtaining primary data include concerns from minority groups about stigmatization, the ethics of harming research subjects by exposing them to violent content, and restrictions on researchers from institutions and governments. Textual analyses of declassified documents from government agencies may overcome these barriers. This article contributes a method for analyzing the predisposing, precipitating, and perpetuating factors of terrorism through open source texts. This method is applied to FBI interrogation summaries of Al Qaeda terrorist Umar Farouk Abdulmutallab who attempted an attack aboard an airplane in 2009. This analysis shows that consuming militant content online led him to narrow his social relationships offline to extremists and foster identifications with subjugated Muslims around the world. After deciding to wage militancy, loyalty to Al Qaeda members, swearing allegiance to and obeying group leaders, and interpreting religious texts militantly perpetuated violent activities. Such work can advance empirical work on militant behavior to develop interventions.

Research examining radical visual communication and its manifestation on the trending platform TikTok is limited. This paper presents a novel methodological framework for studying mobilization strategies of far-right groups on TikTok, employing a mixed-method approach that combines manual annotation, unsupervised image classification, and named-entity recognition to analyze the dynamics of radical visuals at scale. Differentiating between internal and external mobilization, we use popularity and engagement cues to investigate far-right mobilization efforts on TikTok within and outside their community. Our findings shed light on the effectiveness of unsupervised image classification when utilized within a broader mixed-method framework, as each observed far-right group employs unique platform characteristics. While Conspiracists flourish in terms of overall popularity and internal mobilization, nationalist and protest content succeeds by using a variety of persuasive visual content to attract and engage external audiences. The study contributes to existing literature by bridging the gap between visual political communication at scale and radicalization research. By offering insights into mobilization strategies of far-right groups, our study provides a foundation for policymakers, researchers, and online platforms to develop proactive measures to address the risks associated with the dissemination of extremist ideologies on social media.

Societal crises, such as COVID-19, produce societal instability and create a fertile ground for radicalization. Extremists exploit such crises by distributing disinformation to amplify uncertainty and distrust. Based on these developments, this study presents a longitudinal analysis across three different non-violent extremist ideologies in the UK (Islamist, far right and eco-radicals). As part of the study, public social media channels Twitter/X, Facebook, and Telegram) of Britain First, 5 Pillars, and Earth First! were analyzed using a computational language classifier of over 36,000 posts between 2016 and 2021. The increasing prevalence of conspiracy narratives, as well as violent, hateful, and threatening language among the corpus indicates that radicalization dynamics were present and heightened, pre-, during, and post-Pandemic.