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This paper sheds light upon ISIS global-level media operations based on an empirical analysis of 79 official English-language videos using the Braddock and Horgan’s analytical guideline for content analysis. This study results in a quantifiable assessment of videos based on their release dates, language, media centres, duration, musical arrangement, and many other production characteristics, and concludes that ISIS has created a highly sophisticated propaganda apparatus and media operation system with a strong ability to cope with the circumstances and various real-world events that ISIS faces on the ground. Additionally, ISIS’s message contains argumentative moves, and without the knowledge of what these moves are and how they respond to existing dialogues, our understanding of the group’s message in certain dimensions remains partial and incomplete.

A fundamental idea from physics is that macroscopic transitions can occur as a result of an escalation in the correlated activity of a many-body system’s constituent particles. Here we apply this idea in an interdisciplinary setting, whereby the particles are individuals, their correlated activity involves online search activity surrounding the topics of social unrest, and the macroscopic phenomenon being measured are real-world protests. Our empirical study covers countries in Latin America during 2011–2014 using datasets assembled from multiple sources by subject matter experts. We find specifically that the volume and momentum of searches on Google Trends surrounding mass protest language, can detect – and may even pre-empt – the macroscopic on-street activity. Not only can this simple open-source solution prove an invaluable aid for monitoring civil order, our study serves to strengthen the increasing literature in the physics community aimed at understanding the collective dynamics of interacting populations of living objects across the life sciences.

Which news sources do supporters of populist islamophobic groups and their opponents rely on, and how are these sources related to each other? We explore these questions by studying the websites referenced in discussions surrounding Pegida, a right-wing populist movement based in Germany that is opposed to what its supporters regard as islamization, cultural marginalization and political correctness. We draw on a manual content analysis of the news sources and the stances of Twitter users, to then calculate the overlap of sources across audiences. Finally, we perform a cluster analysis of the resulting user groups, based on shared sources. Preferences by language, nationality, region and politics emerge, showing the distinction between different groups among the users. Our tentative findings have implications both for the study of mass media audiences through the lens of social media, and for research on the public sphere and its possible fragmentation in online discourse. This contribution, which is the result of an interdisciplinary collaboration between communication scholars in Germany and journalists in Austria, is part of a larger ongoing effort to understand forms of online extremism through the analysis of social media data

VOX-Pol Blog Post.

The research discusses ISIS’s Media Strategy towards western women by examining
the Tumblr blog UMM LAYTH. Written by a young woman from Scotland who
traveled to the Islamic State, the blog speaks about daily life under ISIS. The paper
gives a background on the author, the content and the blog’s style.

This chapter explores the intersection of white and male supremacy, both of which misrepresent women as genetically and intellectually inferior and reduce them to reproductive and/or sexual functions. The white power movement historically has been characterized by sexism and misogyny, as evidenced by the movement’s attempts to retain European heritage and maintain whiteness by policing the behavior and controlling the bodies of white women. However, the influence of white supremacist discourses on physically violent manifestations of the male supremacist movement remains largely understudied. Using supervised machine learning, we compare a corpus of violent male supremacist manifestos and other multimodal content with highly influential white nationalist texts and the manifestos of violent white supremacists to identify the shared beliefs, tropes and justifications for violence deployed within.