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A framework for understanding Islamic State of Iraq and al-Sham (ISIS)’s apocalyptic theology as an internal strategy to “coercively radicalize” its captive subjects is presented, by comparison to the Lord’s Resistance Army (LRA), which shares key stages of captive indoctrination with ISIS. A violent experience of “entry,” religious rules learned in an “assimilation” process, and millenarian “grand narratives” framing violence as purification, are examined. These stages construct an image of group leaders as divinely endowed with spiritual knowledge and access (i.e., charismatic authority). This can create a sense of dependency on the leaders and their instructions, potentially motivating violent and altruistic behavior from initially unwilling subjects.

This article examines algorithmic content moderation, using the moderation of violent extremist content as a specific case. In recent years, algorithms have increasingly been mobilized to perform essential moderation functions for online social media platforms such as Facebook, YouTube, and Twitter, including limiting the proliferation of extremist speech. Drawing on Katherine Hayles’ concept of “cognitive assemblages” and the Critical Security Studies literature, we show how algorithmic regulation operates within larger assemblages of humans and non-humans to influence the surveillance and regulation of information flows. We argue that the dynamics of algorithmic regulation are more liquid, cobbled together and distributed than it appears. It is characterized by a set of shifting human and machine entities, which mix traditional surveillance methods with more sophisticated tools, and whose linkages and interactions are transient. The processes that enable the consolidation of knowledge about risky profiles and contents are, therefore, collective and distributed among humans and machines. This allows us to argue that the cognitive assemblages involved in content moderation become a cobbled space of preemptive calculation.

VOX-Pol Blog post.

Profiling has its traditions in criminal investigations where it is used to assist in apprehending an offender by examining and attempting to understand his or her psychological motivations and personality. Terrorist specialists and theorists have applied traditional profiling techniques in hopes of distinguishing nonterrorists from terrorists and in an endeavor to understand the motivators for radicalization. However, these attempts have created a divide between the theorists resulting in contradictory data and debate. With the rise of social media, the methods of terrorism have changed. The Islamic State (IS) in particular has tapped into using media, not only to recruit, but as a form of technological combat, which in turn has added to their success and strength. This dissertation introduces the theory of Combat Branding. The findings of this dissertation suggest that it is possible to create a deductive profile of Western IS recruits by beginning with the examination of IS’s Combat Brand. This is a qualitative visual narrative study of official IS media consisting of video and still images. It is my hypothesis that starting with an analysis of the Combat Brand is a missing link to approaching a deductive profile of the intended target audience.

Combating ISIS Online Panelists talked about *ISIS’ use of the Internet and social media to recruit supporters. They also addressed the U.S. government’s efforts to combat the terror group online. Other topics included whether internet and social media companies should be forced to identify and/or ban those suspected of promoting terror.

The Islamic State of Iraq and Syria (ISIS), the Islamic State of Iraq and the Levant (ISIL), or DAISH/DAESH in Arabic is a militant group that has called itself the Islamic State.

Many countries have formulated strategies to combat radicalization, and regulating and monitoring recommendations on social network platforms is part of these strategies. This article explores the concept of radicalization and the proposed causal link between recommendations and radicalization, highlighting some of the key characteristics of recommendation engines. Based on this background potential risk mitigation measures can be identified that directly affect the recommendation engines. To conclude, some key problems and dilemmas for risk management are discussed.