Online Library

The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is threefold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities.

This report contains findings from a study that investigated extreme far right groups’
usages of social media. This was a collaborative project building on an existing
partnership between the Departments of Linguistics and Criminology at Swansea
University, and on the development of a new partnership with the social media
analytics company ‘Blurrt’ (www.blurrt.co.uk). The project was funded by the
CHERISH-DE multidisciplinary research centre at Swansea University
(http://www.cherish-de.uk/) and the School of Arts & Humanities at Edith Cowan
University (https://www.ecu.edu.au/schools/arts-and-humanities).
This report provides an overview of the aims, methodology and key findings of this
project. The project was conducted between January and August 2017. It drew upon
data from two social media platforms (Facebook and Twitter), collected over a 10-
week period (January-April 2017), and concerned two extreme far right groups:
Britain First and Reclaim Australia. Further publications will be appearing in due
course and those interested in hearing more about the project should contact the
report’s authors (details on p.23).

As Al Qaeda and the Islamic State vie for ascendancy in the jihad movement, policymakers grapple with distinguishing the threat posed by these groups. Proceeding from the terrorists’ view of media as a critical arena of jihad, this study applies content analysis to Al Qaeda- and Islamic State-produced magazines in order to empirically differentiate the two groups through the strategies publicized therein. Findings reveal that Al Qaeda consistently employs attrition to compel changes in the West’s policy and behavior, while the Islamic State has shifted from intimidating populations to outbidding competing groups to solidify its claim to the Caliphate.

Because social media sites are self-regulating, each site has developed its own community standards, which serve as regulatory tools. However, the processes of content moderation are often unclear, subjective, and discriminatory. Drawing from a series of interviews with individuals in the “Men Are Scum” movement, this article describes the experiences of women who have been censored on Facebook and explores whether self-regulatory processes on this platform are distinctly gendered. It asserts that both explicit censorship (e.g., limited displays of the body) and implicit censorship (e.g., rampant and unchecked hate speech silencing women’s voices) are operatives on Facebook, limiting women’s expressive potentiality. Thus, this article proposes the term “gender-based censorship” as a lens through which to understand women’s experiences on Facebook. These findings help reveal the pitfalls of industry self-regulation in which profit motives are prioritized over the protection of users (especially those who may be marginalized offline).

This article considers claims made by various authors that the use of filtering and recommendation technology on the Internet can deprive certain communities of feedback, and instead amplify groups’ viewpoints, leading to polarization of opinion across communities, and increases in extremism. The ‘echo chamber’ arguments of Cass Sunstein are taken as representative of this point of view, and examined in detail in the context of a range of research, theoretical and empirical, quantitative and qualitative, in political science and the sociology of religion, from the last quarter century. The conclusion is that the case has not been made either (a) that echo chambers are necessarily harmful, or (b) that the Internet is complicit in their formation.

The presidency of Donald Trump was marked by a period of populist and sometimes extremist right-wing policies and rhetoric, and an escalation of ‘culture wars’ between the left and right in politics and society. He also gained the devoted support of numerous right-wing and so-called alt-right groups, such as QAnon, a conspiracy theory group that first emerged in 2017. This paper takes the novel approach of exploring the existence of a moral panic having formed around the activities of QAnon itself. I suggest that the current regulatory discourses around QAnon are flawed, with insufficient attention being paid to a wider range of extremist groups in assessing how to regulate extremist speech and action.