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The article describes the research network “Analysis of extremist aspirations in social networks (X-SONAR)” funded by the Federal Ministry of Education and Research (BMBF). The network investigates extremist interaction and escalation dynamics in social online networks and their relevance for criminally relevant radicalization processes, such as in the planning and execution of extremist acts of violence. Among other things, the aim is to derive a model for risk assessment and prevention and to develop a demonstrator for a software-based tool that can be used by end-users from security agencies and civil society for on-demand monitoring of extremist agitation in virtual contexts.

This article investigates Twitter data related to the kidnapping case of two German nationals in the southern region of the Philippines by the Abu Sayyaf Group (ASG). It explores perceptions of the ASG, along with associated organizations and sentiments indicated in the tweets together with statistically significant relationships. Findings revealed that: “Rebel” and “Militant” were the most frequently used labels for the ASG; a majority of the tweets contained sentiments that assess threats such as abduction and kidnapping of hostages; and almost half contained words that indicate negotiation or concession to the demands of the captors. Logistic regression analyses on “Rebel” and “Islamist” revealed positive coefficients for these sentiments used as predictors. This meant that people who assessed threats and expressed sentiments that responders should concede to the captors’ demands were more likely to use the “Rebel” or “Islamist” labels. Rather than the two longstanding dominant narratives of the ASG as terrorists and criminals, the emerging rebel and militant labels suggest a more domestically and politically sensitive Twitter commentary than is represented in the work of the Al-Qaeda-centric paradigm exponents. These findings, along with the complex associated political and policy contexts and implications, are discussed in this article.

VOX-Pol Blog Post.

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.

The paper describes a recently-created Twitter corpus of about 6,000 tweets, annotated for hate speech against immigrants, and developed to be a reference dataset for an automatic system of hate speech monitoring. The annotation scheme was therefore specifically designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion, and to offer a broader tagset capable of better representing all those factors, which may increase, or rather mitigate, the impact of the message. This resulted in a scheme that includes, besides hate speech, the following categories: aggressiveness, offensiveness, irony, stereotype, and (on an experimental basis) intensity. The paper hereby presented namely focuses on how this annotation scheme was designed and applied to the corpus. In particular, also comparing the annotation produced by CrowdFlower contributors and by expert annotators, we make some remarks about the value of the novel resource as gold standard, which stems from a preliminary qualitative analysis of the annotated data and on future corpus development.

An introductory note of the Journal ‘Dynamics of Asymmetric Conflicts’