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This report presents the key findings from the London Roundtable on “Understanding the Impact of Terrorist Event Reporting on Countering Violent Extremism”. The event was held at the Australian High Commission in London on 30-31 January 2018. The roundtable brought together media practitioners, CVE and PVE front line practitioners, policy-makers and academics drawn from Australia, the United Kingdom, Belgium, France, Germany, Sweden, Norway, Finland and the United States of America. Other attendees included representatives from various Australian and British Government departments and New Scotland Yard. This report provides summaries of each of the panel discussions that were delivered at the roundtable, before drawing out the key themes, which emerged and policy recommendations.

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The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated. Findings The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong. Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people. In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.

This study seeks to understand the community’s perceptions towards detecting signs of online radicalisation and examine whether different community members would exhibit different levels of understanding. A 57-item survey was administered to 160 undergraduates from Nanyang Technological University (NTU) and 160 Amazon Mechanical Turk (MTurk) workers. Based on the ratings of the 42 online radicalisation indicators identified by Neo (2020), two-factor analyses were separately conducted using oblique rotation to undercover a four-factor structure for the NTU sample and a three-factor solution for the MTurk sample. The results revealed valuable insights into how community members would identify terrorist threats. Furthermore, the survey revealed differences in the participants’ views on the role of the internet in radicalisation pathways and their perceptions regarding various counter-terrorism strategies. Together, the findings would contribute to the discussion of how law enforcement could better engage and work together with the community members to detect terrorist threats.

Criminologists have generally agreed that the Internet is not only a tool or resource for right-wing extremists to disseminate ideas and products, but also a site of important identity work, accomplished interactively through the exchange of radical ideas. Online discussion forums, amongst other interactive corners of the Web, have become an essential conduit for the radical right to air their grievances and bond around their “common enemy.” Yet overlooked in this discussion has been a macro-level understanding of the radical discussions that contribute to the broader collective identity of the extreme right online, as well as what constitutes “radical posting behavior” within this context. Drawing from criminal career measures to facilitate this type of analysis, data was extracted from a sub-forum of the most notorious white supremacy forum online, Stormfront, which included 141,763 posts made by 7,014 authors over approximately 15 years. In study one of this dissertation, Sentiment-based Identification of Radical Authors (SIRA), a sentiment analysis-based algorithm that draws from traditional criminal career measures to evaluate authors’ opinions, was used to identify and, by extension, assess forum authors’ radical posting behaviors using a mixed-methods approach. Study two extended on study one by using SIRA to quantify authors’ group-level sentiment about their common enemies: Jews, Blacks, and LGBTQs. Study three further extended on studies one and two by analyzing authors’ radical posting trajectories with semi-parametric group-based modeling. Results highlighted the applicability of criminal career measures to study radical discussions online. Not only did this mixed-methods approach provide theoretical insight into what constitutes radical posting behavior in a white supremacy forum, but it also shed light on the communication patterns that contribute to the broader collective identity of the extreme right online.

This paper examines the need and possibility for developing online resilience-based approaches in response to COVID-19 vaccine conspiracies, often linked to the far right. Examining three datasets collected between December 2020 and April 2021, this paper details conspiracy narratives that have developed around COVID-19 vaccines, with specific focus on understanding the deployment of the idea of a planned pandemic or so-called ‘#plandemic’. This is then used to consider where existing resilience-based approaches to countering off-line polarisation and extremism might posit an appropriate online response. The article identifies four key #plandemic framings of COVID-19 vaccines — as control, as reset, as unnecessary and as unsafe — and analyses how these themes are constructed, to find that they are often created through hostile and confrontational interaction with other users. Based on these findings, the conclusion suggests companies shift their focus away from ‘negative’ approaches to content moderation (e.g., content removal) and towards resilience-building responses that cultivate flexible individual identities, build community support networks, and/or engage users with national and supranational democratic structures, as a more effective response to the sharing of online conspiracies.