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This article analyses Dabiq magazine to explore the strategic logic of Islamic State (IS) appeals to English-speaking Muslims. It offers the field a conceptual framework through which to analyse IS’s communications strategy and a top-down empirical study of Dabiq’s contents. This paper argues that Dabiq appeals to its audiences by strategically designing in-group identity, Other, solution and crisis constructs which it leverages via value-, crisis- and dichotomy-reinforcing narratives. By fusing identity- and rational-choice appeals, IS provides its audiences with a powerful ‘competitive system of meaning’ that is designed to shape its readership’s perceptions, polarise their support and drive their radicalisation.

The Online Social Network (OSN) has evolved as a popular platform enabling rich topic-centric interactions and serving as a medium to facilitate online radicalization (Behr et al. 2013). Keeping in view the growing need of uncovering online radicalization, we focus on the information network of Twitter and present an approach for identifying dynamic communities, which arise due to “radical” topic-centric user interactions. The approach at successive timestamps deploys evolving topic-entity maps along with evolving interaction graphs. We propose “Rate of Overlap (ROAct)” to determine the similarity among successive community timestamps. We further validate our approach using an open dataset of criminal offences in the city of Denver, Colorado. The approach presented is simple, fast, and effective for dynamic identification of topic-centric communities and, thus, will enable law enforcement agencies to identify hidden radicalization.

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.

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 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.

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.