Online Library

This study investigated the phenomena of group polarisation with particular attention to the differences between offline and online settings. Polarisation is a process that leads people to develop extreme ideologies. Three hundred and seven participants were recruited and randomly assigned to different experimental conditions, i.e. antisocial and prosocial polarisation, within groups of 6 people composed of four confederates, participating in discussions about a social dilemma under two different circumstances: face to face and online. The degree of polarisation was assessed considering the final decisions adopted by the participants, as well as the internal dynamics characterising their final attitudes, i.e. compliance versus conversion. Results showed that online groups appeared more susceptible to polarisation and their members reported a greater degree of conformism. In particular, within online environments, the risk of being polarised, both antisocially and prosocially, increased by around 12%. Furthermore, in an online setting, a greater degree of conversion emerged only when the members decided to adopt a pro-social behaviour, while a greater degree of compliance emerged whenever they decided to adopt antisocial behaviour.

Any intervention in the violent acts of terrorist groups requires accurate differentiation among the groups themselves, which has largely been overlooked in their study beyond qualitative work. To explore the notion of terrorist group differentiation, the online communication of six violent groups were collected: Al-Nusrah Front, al-Qa’ida Central, al-Qa’ida in the Arabian Peninsula, Hamas, Islamic State of Iraq and Syria, and Taliban. All six groups embedded their ideology in digitised documents that were shared through multiple online social networks and media platforms in attempts to influence individuals to identify with their beliefs. The way these groups constructed social roles for their supporters in their ideology was proposed as a novel way to differentiate them and key term extraction was used to find important terms referenced in their communication. Experimental classification was devised to find the highest-ranking roles capable of prediction. Role terms produced high accuracy scores across experiments differentiating the groups (95%CI: 95–98%), with varying inter-group and intra-ideological differences emerging from authority-, religion-, closeness-, and conflict-based social roles. This suggests these constructs possess strong predictive potential to separate terrorist groups through nuanced expressions observed in their communication behaviour and advances our understanding of how these groups deploy harmful ideology.

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.