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Understanding the psychological risk factors in radicalisation and terrorism is typically limited by both a lack of access to individuals who carry out the acts and those…
Understanding the psychological risk factors in radicalisation and terrorism is typically limited by both a lack of access to individuals who carry out the acts and those who are willing to engage in research on the matter. The purpose of this study is to describe the process of self-radicalisation of an otherwise law-abiding individual who engaged in single-actor terrorism activities.
A single case study, based on clinical interviews and psychometric testing, of an individual with autism who engaged in multiple acts of terrorism through online activity. The case is presented within existing frameworks of radicalisation, and describes how it developed along the steps described in the path to intended violence.
A number of variables are identified as contributing towards the individual’s vulnerability to radicalisation, such as deficits in higher order cognition, psychopathology, autism spectrum disorder traits, personal interests, social isolation and life stressors.
Unique to this study is how the process of radicalisation and the possibility to carry out the individual’s attacks was made possible only through the use of internet technology.
Purpose – This chapter examines how those who study issues related to radicalization and counter-radicalization have recently drawn from the experiences of former…
Purpose – This chapter examines how those who study issues related to radicalization and counter-radicalization have recently drawn from the experiences of former extremists to inform our understanding of complex issues in terrorism and extremism studies.
Approach – The authors synthesize the empirical research on radicalization and counter-radicalization that incorporates formers in the research designs. In doing so, the authors trace these research trends as they unfold throughout the life-course: (1) extremist precursors; (2) radicalization toward extremist violence; (3) leaving violent extremism; and (4) combating violent extremism.
Findings – While formers have informed our understanding of an array of issues related to radicalization and counter-radicalization, empirical research in this space is in its infancy and requires ongoing analyses.
Value – This chapter provides researchers, practitioners, and policymakers with an in-depth account of how formers have informed radicalization and counter-radicalization research in recent years as well as an overview of some of the key gaps in the empirical literature.
Purpose – To examine how John Stuart Mill’s harm principle can guide debates surrounding definitions of radicalization, extremism, and deradicalization.Methodology/Approach…
Purpose – To examine how John Stuart Mill’s harm principle can guide debates surrounding definitions of radicalization, extremism, and deradicalization.
Methodology/Approach – This chapter begins by surveying definitional debates in terrorism studies according to three identified binaries: (1) cognitive versus behavioral radicalization; (2) violent extremism versus non-violent extremism; and (3) deradicalization versus disengagement. The author then interprets Mill’s harm principle and assesses which interpretation researchers and policy-makers should favor.
Findings – Applying the harm principle suggests that researchers and policy-makers should prefer behavioral over cognitive radicalization, violent over non-violent extremism, and disengagement over deradicalization. This is because government intervention in people’s lives can be justified to prevent direct risks of harm, but not to change beliefs that diverge from mainstream society.
Originality/Value – This chapter extends previous work that applied the harm principle to coercive preventive measures in counter-terrorism. It makes an original contribution by applying the principle to definitional debates surrounding radicalization and counter-radicalization. The harm principle provides researchers and policy-makers with a compass to navigate these debates. It offers an analytical method for resolving conceptual confusion.
The nature and spread of radical content online -- whether jihadist or right-wing supremicist -- and its link to terror attacks is a source of public and governmental…
Purpose – This chapter examines how sentiment analysis and web-crawling technology can be used to conduct large-scale data analyses of extremist content online.…
Purpose – This chapter examines how sentiment analysis and web-crawling technology can be used to conduct large-scale data analyses of extremist content online.
Methods/approach – The authors describe a customized web-crawler that was developed for the purpose of collecting, classifying, and interpreting extremist content online and on a large scale, followed by an overview of a relatively novel machine learning tool, sentiment analysis, which has sparked the interest of some researchers in the field of terrorism and extremism studies. The authors conclude with a discussion of what they believe is the future applicability of sentiment analysis within the online political violence research domain.
Findings – In order to gain a broader understanding of online extremism, or to improve the means by which researchers and practitioners “search for a needle in a haystack,” the authors recommend that social scientists continue to collaborate with computer scientists, combining sentiment analysis software with other classification tools and research methods, as well as validate sentiment analysis programs and adapt sentiment analysis software to new and evolving radical online spaces.
Originality/value – This chapter provides researchers and practitioners who are faced with new challenges in detecting extremist content online with insights regarding the applicability of a specific set of machine learning techniques and research methods to conduct large-scale data analyses in the field of terrorism and extremism studies.
This chapter draws upon empirical data collected with former violent extremists in the UK to address the phenomenological attractions of engaging in terrorism. We argue…
This chapter draws upon empirical data collected with former violent extremists in the UK to address the phenomenological attractions of engaging in terrorism. We argue that there needs to be more consideration of the attractions of belonging to a terrorist organization and a more thorough appreciation of the experiences that attract people to acts of terrorism. This chapter begins to address these issues by engaging with Jack Katz's (1988) research on the phenomenological foreground, the compelling and seductive qualities of engaging in criminal acts. Katz's highly original and influential research shifts attention away from traditional criminological approaches that emphasize structural background factors such as class, unemployment, gender, poverty, or education. As Katz argues, this structural level of analysis overlooks the subjective phenomenological feelings that accompany criminal behavior. We argue that this is a serious omission as it is precisely the search for thrill, risk, and intense excitement that can serve to motivate further acts of criminality.
Purpose – In order to explore how gender and sexual politics are played out in everyday practice within both the extreme right and jihadi-Salafist movements online, this…
Purpose – In order to explore how gender and sexual politics are played out in everyday practice within both the extreme right and jihadi-Salafist movements online, this chapter analyzes the content of two women’s only forums: The Women’s Forum on Stormfront.org and Women Dawah, a Turkish language pro-IS group chat on Telegram.
Methodology – The Women’s Forum and the Women Dawah data sets were analyzed using structural topic modeling to uncover the differences and similarities in salient topics between White Nationalist and Islamic State women-only forums.
Findings – The cross-ideological and multi-linguistic thematic analysis suggests that the safety of online spaces enables women to be more active, and serves digital support network for like-minding individuals. It also highlights that religion and ideology, whilst interwoven throughout posts on both platforms, they were more explicitly discussed within Women Dawah data.
Originality/Value – This research uses a unique data set which was collected over one year to conduct a cross-ideological and multi-linguistic thematic analysis, a relatively uncommon approach.
The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to…
The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context.
To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated.
The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong.
Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people.
In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.