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Leakage is one of the eight warning behaviors referred to in the violence risk and threat assessment literature. Previous research has highlighted the relevance and prevalence of leakage in lone-actor terrorists; however, a more detailed understanding of this phenomenon is lacking. This study sets out to expand our knowledge of this behavior by conducting an exploratory analysis using court records relating to IS-inspired lone-actor terrorism cases in the United States. The general patterns in leakage warning behavior were analyzed, and different types of leakage were examined with regard to from whom they were leaked, how they were leaked, their presence online, and whether or not they occurred before certain types of attacks more than others. It was found that leakage in the form of support tended to be leaked most frequently to members of the public, via written text and online, whilst the leakage of intent and specifics appeared to be more regularly leaked to co-conspirators and through verbal communication that avoided the online world. Significant relationships were also found between leakage, FBI interaction and attack initiation, but no significant relationship was found between leakage and mental health. The implications of these findings are discussed.

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.

Online social media platforms such as Twitter have been found to be misused by extremist groups, including Islamic State of Iraq and Syria (ISIS), who attract and recruit social media users. To prevent their influence from expanding in the online social media platforms, it is required to understand the online behaviors of these extremist group users and their followers, for predicting and identifying potential security threats. We present an empirical study about ISIS followers’ online behaviors on Twitter, proposing to classify their tweets in terms of political and subjectivity polarities. We first develop a supervised classification model for the polarity classification, based on natural language processing and clustering methods. We then develop a statistical analysis of term-polarity correlations, which leads us to successfully observe ISIS followers’ online behaviors, which are in line with the reports of experts.

The Internet has become an important tool for modern terrorist groups as a means of spreading their propaganda messages and recruitment purposes. Previous studies have shown that the analysis of social signs can help in the analysis, detection, and prediction of radical users. In this work, we focus on the analysis of affect signs in social media and social networks, which has not been yet previously addressed. The article contributions are: (i) a novel dataset to be used in radicalization detection works, (ii) a method for utilizing an emotion lexicon for radicalization detection, and (iii) an application to the radical detection domain of an embedding-based semantic similarity model. Results show that emotion can be a reliable indicator of radicalization, as well as that the proposed feature extraction methods can yield high-performance scores.