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This article aims to demonstrate that a greater understanding of communication in the 21st century is essential to more effective counterterrorism. In fact, while “strategic communication” and “narratives” are advocated by many analysts as essential weapons in countering extremism, few seem to truly understand the reality of the digital-age information environment where such tools need to be deployed. To contribute to bridging this gap, the article outlines some problematic misunderstandings of the contemporary information environment, provides an alternative communication-based framework to explain radicalization, and draws some counterintuitive lessons for tackling terrorism.

We cannot truly understand terrorism in the 21st century—let alone counter it effectively—unless we also understand the processes of communication that underpin it. The book challenges existing terrorism research showing that current approaches are inadequate and outdated. It exposes the fact that, although we live in an age of interconnectedness shaped by media technologies, both policy makers and security experts know very little about how to make sense of this reality. Among the widespread myths the book dispels are: the idea that new recruits into the ranks of al Qaeda are ‘radicalized’ by a ‘narrative of grievance’; that the removal of extremist websites should be a priority; that ‘we’ can ‘rewrite’ terrorists’ propaganda; that being a ‘global brand’ is a source of strength for al Qaeda. This book will be of interest to researchers and students in terrorism studies, communication and media, politics and security.

VOX-Pol Blog post.

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.

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.

Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.