Online Library

This chapter critically examines the role that the Internet and the Internet of Things (IoT) play in violent extremism. The chapter specifically focuses on arguments surrounding radicalisation as a pathway to terrorism and how individuals become radicalised due to different radicalisation processes and theories. Based on this critical analysis, the chapter argues that the Internet plays a key role in radicalisation to violent extremism due to several approaches. Issues highlighted in this chapter mainly focusses on the struggles of social media (SM) moderation and regulations. Furthermore, the chapter discusses how the IoT can aid SM regulation to reduce online radicalisation to violent extremism through artificial intelligence (AI) and machine learning (ML) technology. It will be argued how AI and ML could be deployed to reduce terrorist content online, making social media platforms (SMPs) a safer cyber space for individuals to operate within.

This review will examine the academic literature over which role the internet has in the evolution of extremism and terrorism since its advent. It will compare two different approaches. The first claims that the internet is a major factor that facilitates ideas and narratives, which can lead to the rise of extremism and terrorism. The second, which in its turn contradicts this approach, argues that prior the advent of the internet extremists and terrorists where more successful into achieving their goals. For that reason, the review will be split into 3 sections. The first section will be examining some needed key definitions of what constitutes terrorism and extremism. Afterwards the essay will shift its approach towards the main debate of whether the internet has a causal link with extremism and terrorism or not. Therefore Sect. 2 brings forth the ways, in which the internet has helped terrorism to advance its goals. Moving on to the third section, this piece of work will discuss the approach in which the internet does not assist extremist narratives but, in the contrary helps the advancement of better research around it and its prevention. Lastly, the review will sum up over the literature that has been discussed and conclude that there is always space for future research.

During the 2019 Christchurch attack, the perpetrator livestreamed footage from a helmet-mounted camera. The aesthetic similarity of the attack footage to first-person shooter (FPS) videogames has led to speculation that this might have somehow ‘gamified’ the attack. Generally, the argument for this is that the attack footage (1) imitates or resembles FPS games, gamifying attacks (2) increasing the affective appeal of propaganda by presenting it as play and thereby (3) increasing the salience of these attacks within gaming communities. This article challenges these notions. It argues that the FPS genre should not be associated with such footage due to visual similarity and is better considered in relation to film. The idea that such footage was purposefully shot to look like an FPS is unsupported, and more likely the result of practical considerations. While the framework of gamification might be useful, it should rest on interactivity, rather than aesthetic similarity.

Mainstream risk assessment frameworks such as TRAP-18, ERG22+, VERA-2R, and RADAR largely use Structured Professional Judgement to map individuals against four critical factors; ideology, affiliation, grievance, and moral emotions. However, the growing use of online communication platforms by extremists presents a series of opportunities to complement or extend existing risk assessment frameworks. Here, we examine linguistic markers of morality and emotion in ideologically diverse online discussion groups and discuss their relevance to extant risk assessment frameworks. Specifically, we draw on social media data from the Reddit platform collected across a range of community topics. Nine hundred and eighty-eight threads containing 272,298 individual comments were processed before constructing high-order models of moral emotions. Emotional and moral linguistic content was then derived from these comments. We then conducted comparisons of linguistic content between mainstream left and right political discourse, anti-Muslim (far-right), Men’s Rights (Incel-like), and a nonviolent apolitical control group. Results show that a combination of individualising moral communication and high emotionality separate far-right and Incel-like groups from mainstream political discourse and provide an early warning opportunity.

While the interest in Salafi-jihadism and the digital arena is not a new phenomenon, a limited amount of research has focused on the specific strategies and narratives disseminated by local groups and online supporter communities at the national and international level. The editors provide a brief introduction to the issue of Salafi-jihadism and digital media within the Swedish context, and further define the aim and research questions of the volume at hand. In addition, the current chapter defines some of the key concepts used throughout the volume, and further discusses some additional methodological considerations.

Purpose
With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.
Design/methodology/approach
This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.
Findings
Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.
Originality/value
At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.