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“Never before in history have terrorists had such easy access to the minds and eyeballs of
millions,” declared one journalistic account of the Islamic State’s propaganda machine and
proficient use of Twitter, Facebook, bots, and other modern means of getting its message out.
Such views that the group’s “mastery of modern digital tools” has transformed terrorism
are commonplace and, though usually presented breathlessly, contain some basic truths.1
Successful terrorist groups are good communicators and they employ the technology of
their times. Fighting terrorism today thus requires fighting terrorism on the Internet and
otherwise countering the use of advanced communications technologies. President Trump
himself stressed this in a tweet after a 2017 terrorist attack in London: “Loser terrorists must
be dealt with in a much tougher manner. The internet is their main recruitment tool which
we must cut off & use better!”2 Terrorists are only one dangerous actor on the Internet—and
the one this paper focuses on—but other dangers ranging from hostile state intelligence
services to criminal groups are also lurking. The above journalist’s quote could also apply to
Russian disinformation, sophisticated criminal phishing attempts, and other malicious uses
of the Internet.
This paper proceeds as follows. First, it examines some of the ways in which terrorist groups
use the Internet, focusing on the Islamic State in particular, and the limits and problems
they have had. Second, it looks at several of the historical problems the US government
has had in stopping this use and at the general issues that are likely to plague future efforts
regarding terrorist use of new technologies. Finally, the paper details some of the parameters
of an Intelligence Reserve Corps, describing its benefits and its limits.

Radical right influencers routinely use social media to spread highly divisive, disruptive and anti-democratic messages. Assessing and countering the challenge that such content poses is crucial for ensuring that online spaces remain open, safe and accessible. Previous work has paid little attention to understanding factors associated with radical right content that goes viral. We investigate this issue with a new dataset (ROT) which provides insight into the content, engagement and followership of a set of 35 radical right influencers. It includes over 50,000 original entries and over 40 million retweets, quotes, replies and mentions. We use a multilevel model to measure engagement with tweets, which are nested in each influencer. We show that it is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors, including the number of followers each influencer has, the type of content (original posts, quotes and replies), the length and toxicity of content, and whether influencers request retweets. We make ROT available for other researchers to use.

The present paper represents an exploratory, dynamic and qualitative application of Social Network Analysis (SNA) for modelling the development of Islamist terror cells in the West. Two well‐known case studies are systematically re‐examined using this methodology as a supporting framework for interpreting the sequence of group development from a social psychological perspective. By drawing attention to salient features of evolving network structures, insight is gained into group functioning and underlying social–psychological mechanisms of radicalisation. This article represents a starting point for giving greater methodological and theoretical recognition to the dynamic structural properties of Islamist terrorist groups in the West. It is intended to stimulate discussion and ideas for future, more rigorous research.

Terrorism, crime, and war are all familiar notions; however, the way in which these have been altered through cyberspace is not yet fully, nor unanimously, understood through definitions, theories, and approaches. Although the threat level of terrorism in the UK has lowered to moderate, the threat posed by cyber terrorism has nonetheless heightened throughout the COVID pandemic due to the greater necessity and presence of technology in our lives. This research aimed to highlight the necessity for a unanimous cyber terrorism definition and framework and further aimed to determine what perceptions are held by the general public regarding cyber terrorism through a mixed methods approach. The literature review confirms that there is an absence of a unanimously agreed upon definition of cyber terrorism, and furthermore that the existing academic definitions are not compatible with UK legislation. In addition, the literature review highlights an absence of a cyber terrorism framework that classifies what kind of terrorist activity is cyber enabled or cyber dependent. Quantitative data from the online survey find a couple of significant effects implying the necessity for greater diversity amongst stakeholders which could potentially enhance the detection and prevention of terrorism in the UK. The qualitative data find that although there is some agreement amongst the sample population in views held towards cyber terrorism, some misconceptions are nonetheless present which could have implications on the general public’s ability to identify and report cyber terrorist activity. Overall, the findings from the literature review and the primary data collection aid in developing a cyber terrorism definition that is compatible with UK legislative definitions, and further aids in developing a terrorist activity framework that succinctly highlights the inextricable links between traditional, cyber enabled, and cyber-dependent terrorism.

Incels, a portmanteau of the term involuntary celibates, operate in online communities to discuss difficulties in attaining sexual relationships. Past reports have found that multiple elements of the incel culture are misogynistic and favorable towards violence. Further, several violent incidents have been linked to this community, which suggests that incel communities may resemble other ideologically motivated extremist groups. The current study employed an inductive qualitative analysis of over 8,000 posts made in two online incel communities to identify the norms, values, and beliefs of these groups from a subcultural perspective. Analyses found that the incel community was structured around five interrelated normative orders: the sexual market, women as naturally evil, legitimizing masculinity, male oppression, and violence. The implications of this analysis for our understanding of extremism and the role of the internet in radicalization to violence are considered in depth.

The dramatic growth of the Web has motivated researchers to extract knowledge from enormous repositories and to exploit the knowledge in myriad applications. In this study, we focus on natural language processing (NLP) and, more concretely, the emerging field of affective computing to explore the automation of understanding human emotions from texts. This paper continues previous efforts to utilize and adapt affective techniques into different areas to gain new insights. This paper proposes two novel feature extraction methods that use the previous sentic computing resources AffectiveSpace and SenticNet. These methods are efficient approaches for extracting affect-aware representations from text. In addition, this paper presents a machine learning framework using an ensemble of different features to improve the overall classification performance. Following the description of this approach, we also study the effects of known feature extraction methods such as TF-IDF and SIMilarity-based sentiment projectiON (SIMON). We perform a thorough evaluation of the proposed features across five different datasets that cover radicalization and hate speech detection tasks. To compare the different approaches fairly, we conducted a statistical test that ranks the studied methods. The obtained results indicate that combining affect-aware features with the studied textual representations effectively improves performance. We also propose a criterion considering both classification performance and computational complexity to select among the different methods.