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VOX-Pol Blog Post.

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.

The project Mapping Networks and Narratives of Online Right-Wing Extremists in New South Wales (NSW) used the systematic mining and analysis of online data to generate evidence-based insights into online right-wing extremism (RWE) across the state. The project was conducted between July 2019 and February 2020 with data collection occurring from August to November 2019. The project addressed three key areas:

– What is the nature of the online RWE environment in NSW?
– How are themes and narratives framed in different online contexts in order to mobilise support?
– What level of risk does the online RWE environment pose?

The research areas were framed as broad questions to facilitate wide exploratory research into the online RWE movement in NSW, a milieu that has been little studied. This breadth of scope was considered pertinent in the wake of the March 2019 mass casualty terrorist attack in Christchurch, New Zealand, by an attacker originating from NSW.

The rise of the Internet has dramatically increased the degree to which youth may be exposed to online hate content, and simplified the process of sharing this content with others. Viewing messages that contain hate speech or language vilifying others can increase an individual’s risk of radicalization to extremist views and the acceptance of violent ideologies. Researchers have begun to explore the risk factors for exposure to such content, with prior studies demonstrating a relationship between low self-control and online activities being important correlates. Few studies have utilized youth samples to assess these relationships, or explored the voluntary consumption and sharing of content. This study attempts to address this gap in the literature using self-report responses provided by a sample of 1,193 youths in South Australia. A series of quantitative models are estimated assessing the relationships between self-control, opportunities to view content using both on and off-line measures, and four dependent variables related to exposure to or sharing of hate content. The implications of this analysis for our understanding of the utility of criminological theory to radicalization and countering violent extremism are discussed in detail.

Psychological theories of mobilization tend to focus on explaining people’s motivations for action, rather than mobilization (“activation”) processes. To investigate the online behaviors associated with mobilization, we compared the online communications data of 26 people who subsequently mobilized to right-wing extremist action and 48 people who held similar extremist views but did not mobilize (N = 119,473 social media posts). In a three-part analysis, involving content analysis (Part 1), topic modeling (Part 2), and machine learning (Part 3), we showed that communicating ideological or hateful content was not related to mobilization, but rather mobilization was positively related to talking about violent action, operational planning, and logistics. Our findings imply that to explain mobilization to extremist action, rather than the motivations for action, theories of collective action should extend beyond how individuals express grievances and anger, to how they equip themselves with the “know-how” and capability to act.