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As social media becomes an integral part of our daily lives, and groups seek to utilize this medium to facilitate activism, understanding the nature of these communications and the impact of the content on the individual user becomes a valid area of interest. When one then considers that extremist and terrorist groups have found social media to be an inexpensive and effective means for communication, radicalization, recruitment and member mobilization, the need for this understanding becomes critical. This research seeks to provide just such an understanding in its examination of Far-Right English Defence League and their use of Facebook during a period of increased activism and online growth. Important elements of this work include an understanding of the legal and ethical issues surrounding the collection of online content, particularly in extremist environments; the role of traditional media in their coverage of the group and whether the comments of the members reflect the group’s mission statement of the characterization of traditional media; the ability to enhance data segregation and analysis through the development and use of specialized software; and most importantly the findings from the data analysis. Contained within these findings is an understanding of the intricacies of online participation in extremist social media. These include insights into overall traffic generation, the use of links within communications and their impact on the member traffic, and how the group narrative put forth by the administrator is reflected in the dialogue of the users. The most important finding was an understanding of individual user participation within the group and how, even with such an inexpensive and pervasive media outlet, activist groups still struggle to overcome the problem of participation. That this knowledge can be applied in a meaningful way in counter extremist and counterterrorism efforts was an interesting and satisfying development.

Notwithstanding inherent differences between the counterterrorist financing regime and the regulatory regime governing communication service providers, there are clear benefits in taking lessons learnt from longstanding efforts on terrorist financing into account when developing a response to the online terrorist threat. Lawmakers developing a regulatory regime for communication service providers (CSPs) should engage with their counterparts involved in the response to terrorist financing to understand potential unintended consequences of this regime, including counterproductive incentives, risk displacement and other factors identified in this paper.

Advancements in big data analytics offer new avenues for the analysis and deciphering of suspicious activities on the internet. One promising new technology to increase the identification of terrorism threats is based on probabilistic computing. The technology promises to provide more efficient problem solutions in encryption and cybersecurity. Probabilistic computing technologies use large amounts of data, though, which raises potential privacy concerns. A study (N = 1,023) was conducted to survey public support for using probabilistic computing technologies to increase counterterrorism efforts. Overall, strong support was found for the use of publicly available personal information (e.g., personal websites). Regarding private personal information (e.g., online conversations), respondents perceived it to be more appropriate to use information from out-group members (non-American citizens) than from in-group members (American citizens). In line with a social-identity account, this form of in-group favoritism was strongest among respondents displaying a combination of strong national identities and strong privacy concerns.

Dr Prucha discusses how to understand ‘Islamic State’ narratives by analysing their media output. The lecture was organised by VOX-Pol partner institute, International Centre for the Study of Radicalisation (ICSR) and held at King’s College London on 6 July 2015. It focuses on the symbiotic relationships between ideology, media content and strategic action, and on how different messages are targeted at different audiences, was broadcast live on Periscope to wide enthusiasm.

The recent rise of the political extremism in Western countries has spurred renewed interest in the psychological and moral appeal of political extremism. Empirical support for the psychological explanation using surveys has been limited by lack of access to extremist groups, while field studies have missed psychological measures and failed to compare extremists with contrast groups. We revisit the debate over the psychological and moral appeal of extremism in the U.S. context by analyzing Twitter data of 10,000 political extremists and comparing their text-based psychological constructs with those of 5000 liberal and 5000 conservative users. The results reveal that extremists show a lower positive emotion and a higher negative emotion than partisan users, but their differences in certainty is not significant. In addition, while left-wing extremists express more language indicative of anxiety than liberals, right-wing extremists express lower anxiety than conservatives. Moreover, our results mostly lend support to Moral Foundations Theory for partisan users and extend it to the political extremists. With the exception of ingroup loyalty, we found evidences supporting the Moral Foundations Theory among left- and right-wing extremists. However, we found no evidence for elevated moral foundations among political extremists.

Identifying and profiling threat actors are high priority tasks for a number of governmental organizations. These threat actors may operate actively, using the Internet to promote propaganda, recruit new members, or exert command and control over their networks. Alternatively, threat actors may operate passively, demonstrating operational security awareness online while using their Internet presence to gather information they need to pose an offline physical threat. This paper presents a flexible new prototype system that allows analysts to automatically detect, monitor and characterize threat actors and their networks using publicly available information. The proposed prototype system fills a need in the intelligence community for a capability to automate manual construction and analysis of online threat networks. Leveraging graph sampling approaches, we perform targeted data collection of extremist social media accounts and their networks. We design and incorporate new algorithms for role classification and radicalization detection using insights from social science literature of extremism. Additionally, we develop and implement analytics to facilitate monitoring the dynamic social networks over time. The prototype also incorporates several novel machine learning algorithms for threat actor discovery and characterization, such as classification of user posts into discourse categories, user post summaries and gender prediction.