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This article exposits and contrasts the roles of two diasporas in ethnic conflict waged in their homelands, namely the Sikh diaspora’s involvement in the Punjab insurgency in north India and the Sri Lankan Tamil diaspora’s role in Sri Lanka’s Tamil insurgency. It draws out the various similarities and distinctions between the two in their use of technology, means of mobilization and identity production, and the geographical and political reach of their institutional arrangements. The article argues that the varying means by which these diasporas came into being affected the ways in which they mobilized and the positions they espoused towards homeland politics. It finds that the abilities of the two diasporas to contribute to events “back home” differed in part because of the scope of their respective institutional arrangements.

While the use of the internet and social media as a tool for extremists and terrorists has been well documented, understanding the mechanisms at work has been much more elusive. This paper begins with a grounded theory approach guided by a new theoretical approach to power that utilises both terrorism cases and extremist social media groups to develop an explanatory model of radicalisation. Preliminary hypotheses are developed, explored and refined in order to develop a comprehensive model which is then presented. This model utilises and applies concepts from social theorist Michel Foucault, including the use of discourse and networked power relations in order to normalise and modify thoughts and behaviors. The internet is conceptualised as a type of institution in which this framework of power operates and seeks to recruit and radicalise. Overall, findings suggest that the explanatory model presented is a well suited, yet still incomplete in explaining the process of online radicalisation.

VOX-Pol Blog Post.

Since the beginning of 2021, ISD Germany has been researching right-wing extremist actors on alternative platforms on the internet. Three reports were published as part of the German Federal Ministry for Justice (BMJ)-funded project “Countering radicalisation in right-wing extremist online subcultures”. The last report in 2021, “Detours and Diversions – Online Strategies for the Dissemination of Right-Wing Extremist Content”, provides a summary of the projects central findings and presents them in a comparative manner.

This report thus combines the methodological and theoretical groundwork of the report “Wegweiser” with the results of the empirical research of the reports “Fluchtwege” and “Stützpfeiler Telegram”, and puts them into context. The report presents a comparison of data from established platforms and Telegram, and thus helps to gain a better understanding of the behaviour of far-right actors on the selected platforms. It also includes a comparative analysis of the strategies and linkages of far-right and radical right actors on established and alternative platforms. This research is based on the empirical data collected by the platforms. Given, that data collection is different for each platform, and given that this project also explores alternative platforms, it also furthers the exploration of data collection options, which are described in more detail in this report.

Some scholars and others are sceptical of a significant role for the Internet in processes of violent radicalisation. There is increasing concern on the part of other scholars, and increasingly also policymakers and publics, that easy availability of violent extremist content online may have violent radicalising effects. This article identifies a number of core questions regarding the interaction of violent extremism and terrorism and the Internet, particularly social media, that have yet to be adequately addressed and supplies a series of six follow-up suggestions, flowing from these questions, for progressing research in this area. These suggestions relate to (1) widening the range of types of violent online extremism being studied beyond violent jihadis; (2) engaging in more comparative research, not just across ideologies, but also groups, countries, languages, and social media platforms; (3) deepening our analyses to include interviewing and virtual ethnographic approaches; (4) up-scaling or improving our capacity to undertake “big data” collection and analysis; (5) outreaching beyond terrorism studies to become acquainted with, for example, the Internet Studies literature and engaging in interdisciplinary research with, for example, computer scientists; and (6) paying more attention to gender as a factor in violent online extremism. This research was produced with the aid of VOX-Pol Research Mobility Programme funding and supervision by VOX-Pol colleagues at Dublin City University.

Social networks are being used by terrorist organizations to distribute messages with the intention of influencing people and recruiting new members. The research presented in this paper focuses on the analysis of Twitter messages to detect the leaders orchestrating terrorist networks and their followers. A big data architecture is proposed to analyze messages in real time in order to classify users according to diferent parameters like level of activity, the ability to infuence other users, and the contents of their messages. Graphs have been used to analyze how the messages propagate through the network, and this involves a study of the followers based on retweets and general impact on other users. Ten, fuzzy clustering techniques were used to classify users in profiles, with the advantage over other classifcations techniques of providing a probability for each profile instead of a binary categorization. Algorithms were tested using public database from Kaggle and other Twitter extraction techniques. The resulting profiles detected automatically by the system were manually analyzed, and the parameters that describe each profile correspond to the type of information that any expert may expect. Future applications are not limited to detecting terrorist activism. Human resources departments can apply the power of profle identification to automatically classify candidates, security teams can detect undesirable clients in the financial or insurance sectors, and immigration officers can extract additional insights with these
techniques.