Online Library

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.

This briefing is the first in ISD’s Gaming and Extremism Series exploring the role online gaming plays in the strategy of far-right extremists in the UK and around the world. This is part of a broader programme on the “Future of Extremism” being delivered by ISD in the second half of 2021, charting the transformational shifts in the extremist threat landscape two decades since 9/11, and the policy strategies required to counter the next generation of extremist threats. The series will include reports exploring extremist activity on four gaming-related platforms: Steam, Discord, DLive and Twitch.

This study explores the internal dynamics and networks of terrorist groups in cyberspace—in particular, Al-Qaeda and the Islamic State of Iraq and al-Sham (ISIS). Using a “Global Cyberterrorism Dataset” that features data on cyberterror attacks between 2011 and 2016, this research analyzes these two terrorist groups through the lens of a cyber-conflict theory that integrates conflict theory with Jaishankar’s space transition theory. Through a network analysis methodology, we examine the invisible relationships and connections between the national origins and target countries of cyberterror attacks. The analysis focuses on the networks of national origins of terrorists and victims, network structures of Al-Qaeda and ISIS actors, and clustering networks of Al-Qaeda and ISIS cyberterrorists. Results indicate that terror in cyberspace is ubiquitous, more flexible than traditional terrorism, and that cyberattacks mostly occurred within the countries of origin. We conclude by discussing the complex features of cyberterror networks and identify some of the geostrategic implications of the divergent cyber strategies adopted by Al-Qaeda and ISIS.

This article contributes to the growing literature on extremist and terrorist online ecologies and approaches to snapshotting these. It opens by measuring Twitter’s differential disruption of so-called “Islamic State” versus other jihadi parties to the Syria conflict, showing that while Twitter became increasingly inhospitable to IS in 2017 and 2018, Hay’at Tahrir al-Sham and Ahrar al-Sham retained strong communities on the platform during the same period. An analysis of the same groups’ Twitter out-linking activity has the twofold purpose of determining the reach of groups’ content by quantifying the number of platforms it was available on and analyzing the nature and functionalities of the online spaces out-linked to.

This study offers an empirical insight into terrorists’ use of the Internet. Although criminology has previously been quiet on this topic, behavior‐based studies can aid in understanding the interactions between terrorists and their environments. Using a database of 231 US‐based Islamic State terrorists, four important findings are offered: (1) This cohort utilized the Internet heavily for the purposes of both networking with co‐ideologues and learning about their intended activity. (2) There is little reason to believe that these online interactions are replacing offline ones, as has previously been suggested. Rather, terrorists tend to operate in both domains. (3) Online activity seems to be similar across the sample, regardless of the number of co‐offenders or the sophistication of attack. (4) There is reason to believe that using the Internet may be an impediment to terrorists’ success.

The paper explores how visual affective practice is used to spread and bolster a nationalist, extremist and racist ethos on the public Facebook page of the anti-immigrant group, Soldiers of Odin. Affective practice refers to a particular sensibility of political discourse, shaped by social formations and digital technologies—the contexts in which political groups or communities gather, discuss and act. The study shows how visual affective practice and sharing and responding to images fortify moral claims, sense exclusionary solidarity and promote white nationalist masculinity which legitimizes racist practices of “soldiering.” By examining both the representations and their reactions (emoticons), the study demonstrates how ideas and values are collectively strengthened through affective sharing and are supported by platform infrastructures. Most importantly, it demonstrates that instead of considering the affect of protecting the nation as a natural result of “authentic” gut feeling, we should understand the ways it is purposefully and collectively produced and circulated.