Online Library

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.

Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.

This report offers a preliminary analysis of the effectiveness of network disruptions in achieving one specific outcome: tackling terrorist violence. It analyses the relationship between network disruptions and deaths and injuries from terrorist attacks to determine whether there is support for the commonly made argument that network disruptions are an important counterterrorism tactic. Using a panel dataset of daily incidents of national-level network disruptions and terrorist attacks globally between 2016 and 2019, a fixed effects regression model shows that national-level network disruptions are not correlated with the number of people killed or injured in terrorist attacks. In addition, there is no correlation between a ban on social media platforms – specifically Facebook, Twitter and WhatsApp – and deaths or injuries from terrorist violence. This analysis has some limitations that make it difficult to make a causal claim, such as the non-random assignment of the treatment (that is, network disruptions) and the absence of a control variable to capture increased security around network disruptions. In general, these findings offer another perspective on the debate on network shutdowns, which often centres on the implications of shutdowns for human rights and democratic engagement and does not typically delve into empirical evidence on what network shutdowns can or cannot accomplish.

Contemporary research has highlighted the steady rise of individuals becoming radicalised via exposure to extremist discussion on the internet, with the ease of communication with other users that the internet provides playing a major role in the radicalisation process of these individuals. The aim of the present systematic review was to explore recent research into the utilisation of language in extremist cyberspaces and how it may influence the radicalisation process. The findings suggest that there are five prominent linguistical behaviours adopted by extremists online: Algorithmic, Conflict, Hate, Positive, and Recruitment. The results demonstrate that the main purpose of extremist language online is to shape the perceptions of users to see their associated group in positive regard, while simultaneously negatively framing outgroup opposition. This is then followed by encouraging conflict against the promoted ideologies’ perceived enemies. Limitations, future research, and implications are discussed in detail.