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The troublesome question of how and whether to consider what are commonly referred to as Lone Wolf terrorists within the broader roster of terrorist groups is something that has regularly confounded security analysts for a variety of reasons. This article attempts to create some sort of typology to start to define the group, with specific reference to the instances of Lone Wolves (or Lone Wolf Packs, an admittedly paradoxical choice of words that is defined in the article as small, isolated groups of individuals involved in terrorism) who claim to adhere to an extremist Islamist ideology. The article offers four subsets to the definition, drawing upon a detailed analysis of a variety of different plots in Europe and North America: Loner, Lone Wolf, Lone Wolf Pack, and Lone Attacker. The purpose of the article is to offer some preliminary thoughts on the issue of Lone Wolves, and start a process towards deeper understanding and closer analysis of the phenomenon. This is of particular salience given the frequency with which security analysts cite the phenomenon as a threat and the increasing way in which Al Qaeda ideologues refer to it.

VOX-Pol Blog post.

In recent years, the media department of the self-proclaimed Islamic State has proven itself to be highly adept at strategic communication. While much research has gone into the group’s digital and online capabilities, there remains a significant gap in the knowledge regarding its in-country propaganda operations and objectives. In recognition of this, the following research paper approaches the issue from a different angle, attempting to better understand how and why the group communicates its brand through the lens of two publications – al-Naba’, its Arabic-language newspaper, which appears to be designed primarily for offline dissemination in the caliphate itself, and Rumiyah, its foreign-language electronic magazine, which has only ever appeared online. Using content analysis to identify and compare each publication’s internal (local) and external (global) media priorities over the four-month period between September and December 2016, we develop an empirical evaluation of the group’s recent forays into targeted outreach.

Since the 2016 US Presidential Election, extreme right-wing communities have gained extensive popularity on YouTube, spreading discourses of white supremacy and conspiracy. This paper focuses on how methods drawn from Systemic Functional Linguistics (SFL) can be used to analyze this communication and contribute to research interests within the field of media and communication studies. SFL is a social semiotic model of language concerned with systematic analysis of language choices in terms of their social context. More specifically, this paper draws upon the Appraisal and Affiliation frameworks developed within SFL, in order to understand how patterns of evaluation are expressed in language and how these function in terms of aligning ambient audiences with particular values. YouTube videos and comments about the Notre Dame Cathedral Fire are used as a case study. The aim is to illustrate how this approach can offer an additional perspective on the issues of information disorder and hate speech that does not attempt to homogenize the multiple reasons why people engage in such hateful behavior.

The design of the Internet has made it an especially useful tool to terrorist groups. Using the Internet, terrorist groups have been especially successful in recruiting new members and exciting them into action. Previous works have provided comprehensive accounts of ways that terrorists use the Internet. This article expands on the understanding of this phenomenon by framing the previous works in a systematic model of terrorist’s use of the Internet through social learning theory. The article also offers counterterrorist strategies in accordance with the components of social learning.

This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.