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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.

VOX-Pol Blog post.

VOX-Pol Blog post.

Policy Exchange’s new report provides a comprehensive analysis of the
struggle against online jihadist extremism – what we call “the new
Netwar”. This issue is vital to UK national security and there is a
danger that the blood and treasure we are investing in defeating ISIS in
Iraq and Syria will produce little more than a pyrrhic victory unless we
act to defeat the virtual threat. At present, we are certainly not winning
the war online. The spate of terrorist attacks the UK suffered in the first
half of 2017 confirmed that online extremism is a real and present
danger. In each case, online radicalisation played some part in driving
the perpetrators to violence. As a society, we are struggling to grasp the
extent of the challenge and also appropriate ways of responding. It is
clear that the status quo is not working. It is time for a new approach.
Policy Exchange has worked with a team of experts to provide fresh
insight into the debate around online extremism. The report that
follows is divided into three sections:

The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.