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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.

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.

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.

This systematic review seeks to position online radicalisation within whole system frameworks incorporating individual, family, community and wider structural influences, whilst reporting evidence of public mental health approaches for individuals engaging in radical online content. Methods: Authors searched Medline (via Ovid), PsycInfo (via Ebscohost) and Web of Science (Core Collection), with the use of Boolean operators across ‘extremism’, ‘online content’, and ‘intervention’. Results: Following assessment of full text, all retrieved papers had to be excluded. Results from six excluded articles which did not fit inclusion criteria but identified theoretical relationships between all three elements of online extremism, psychological outcomes, and intervention strategy, were discussed. Authors found no articles outlining public mental health approaches to specifically online radicalisation. Conclusions: There is an immediate need for further research in this field given the increase in different factions of radicalised beliefs resulting from online, particularly social media, usage.

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.

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.