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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.

Far right populist politicians and movements have secured high levels of visibility thanks to often compliant media outlets and unregulated digital platforms. The pursuit of media coverage and the communication of rage are no longer incidental but essential to the growth of reactionary populisms. Yet, while prominent liberal media outlets are ‘aghast’ at events such as the election of Donald Trump and the Brexit vote, little attention has been paid to the structural conditions and policy frameworks that have facilitated the circulation of clickbait and misinformation, together with the incessant coverage of their leaders, that have been exploited by far right movements. This article identifies four areas of ‘media policy failure’ that have nurtured highly skewed media environments and concludes by calling for a new policy paradigm based around redistribution that aims to reconstruct media systems in order to undermine the appeal of populist forces on the far right.

This Whitehall Report compares two P/CVE programmes in Kenya and Lebanon that independently came to the same conclusion: to counter the multiplicity of factors drawing young people into violent extremism, communications and media tools should be recast to serve the needs of young people, rather than treat them as an audience.

In this brief, ARTICLE 19 seeks to contribute to discussions on greater regulation of social media platforms, including calls for such platforms to be considered publishers. We do so by exploring a possible model for the independent and effective self regulation of social media platforms.

ARTICLE 19 recognises that dominant social media companies hold considerable power over the flow of information and ideas online, given the vast quantities of content published on their platforms. The way in which social media companies have dealt with content issues on their platforms, especially around ‘hate speech’, has been of particular concern to many stakeholders. Under international standards on freedom of expression, however, it is a fairly complex task to decide whether a specific message can be identified as unlawful ‘hate speech’, and, as such, whether it should or could legitimately be prohibited. More generally, any restriction on freedom of expression, whatever the objective it seeks to achieve, necessarily raises a series of legal questions.

The use of the Internet as a vehicle of socialization has exploded in the 21st century and while this presents exciting possibilities, it also comes with troubling ones. Among those who have embraced this new medium are extremist groups, who use the Web as a space in which to communicate, exchange ideas, network and reach new followers. In regards to this last point, it provides them near unlimited opportunities to gain access to potential recruits and converts, raising interesting questions in terms of the dynamics of the radicalization process and how it manifests itself within the confines of this new arena. Through an analysis of postings made to the discussion board of Stormfront.org, this research paper examines whether or not the radicalization process follows the same transformative pattern in the virtual world as has been described in some of the leading academic theories that address this phenomenon in the physical one.

Extremist online networks reportedly tend to use Twitter and other Social Networking Sites (SNS) in order to issue propaganda and recruitment statements. Traditional machine learning models may encounter problems when used in such a context, due to the peculiarities of microblogging sites and the manner in which these networks interact (both between themselves and with other networks). Moreover, state-of-the-art approaches have focused on non-transparent techniques that cannot be audited; so, despite the fact that they are top performing techniques, it is impossible to check if the models are actually fair. In this paper, we present a semi-supervised methodology that uses our Discriminatory Expressions algorithm for feature selection to detect expressions that are biased towards extremist content (Francisco and Castro 2020). With the help of human experts, the relevant expressions are filtered and used to retrieve further extremist content in order to iteratively provide a set of relevant and accurate expressions. These discriminatory expressions have been proved to produce less complex models that are easier to comprehend, and thus improve model transparency. In the following, we present close to 70 expressions that were discovered by using this method alongside the validation test of the algorithm in several different contexts.