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Islamophobic hate speech on social media is a growing concern in contemporary Western politics and society. It can inflict considerable harm on any victims who are targeted, create a sense of fear and exclusion amongst their communities, toxify public discourse and motivate other forms of extremist and hateful behavior. Accordingly, there is a pressing need for automated tools to detect and classify Islamophobic hate speech robustly and at scale, thereby enabling quantitative analyses of large textual datasets, such as those collected from social media. Previous research has mostly approached the automated detection of hate speech as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically informed social science and effective monitoring of social media platforms. Drawing on in-depth conceptual work we build an automated software tool which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. Our tool enables future quantitative research into the drivers, spread, prevalence and effects of Islamophobic hate speech on social media.

Many researchers, practitioners, and policy-makers continue to raise questions about the role of the Internet in facilitating violent extremism and terrorism. A surge in research on this issue notwithstanding, relatively few empirically grounded analyses are yet available. This chapter provides researchers with five key suggestions for progressing knowledge on the role of the Internet in facilitating violent extremism and terrorism so that we may be better placed to determine the significance of online content and activity in the latter going forward. These five suggestions relate to (1) collecting primary data across multiple types of populations; (2) making archives of violent extremist online content accessible for use by researchers and on user-friendly platforms; (3) outreaching beyond terrorism studies to become acquainted with, for example, the Internet studies literature and engaging in interdisciplinary research with, for example, computer scientists; (4) including former extremists in research projects, either as study participants or project collaborators; and (5) drawing connections between the on- and offline worlds of violent extremists.

In its offline aspect the broader right-wing movement is comprised of a range of groups and idealogical variances that have traditionally had difficulty coalescing into a coherent movement with broad appeal. In its online aspect, right-wing extremist practice is focused on spreading ideaology, recruiting and radicalization, and building transnational communities.

Eco-fascism is emerging at both the highest levels of state and the lowest reaches of the political underworld. However, this may be only part of a much larger, more idealogically complex, emerging extremist threat. The climate crisis–and the crisis of global financial capitalism from which it is inextricable–may yet be driving a realignment of extremist environmental politics. An exploratory analysis of radical environmentalist discourse on the Twitter platform reveals the emergence of an ecological extremism that confounds contemporary understandings of the left, right, authoritarian and liberal. If this represents the future of eco-extremism, it may be necessary for researchers and practitioners to reorient the frameworks that guide their assessment of emerging risks.

The Internet’s relatively unfettered transmission of information risks exposing individuals to extremist content. Using online survey data (N = 768) of American youth and young adults, we examine factors that bring individuals into contact with online material advocating violence. Combining aspects of social structure-social learning theory with insights from routine activity theory, we find that exposure to violence-advocating materials is positively correlated with online behaviors, including the use of social media platforms and the virtual spaces individuals frequent. Target antagonism is also correlated with exposure to violence-advocating materials, but guardianship and online and offline associations are not. Finally, feelings of dissatisfaction with major social institutions and economic disengagement are associated with exposure to violent materials online.

Identifying and profiling threat actors are high priority tasks for a number of governmental organizations. These threat actors may operate actively, using the Internet to promote propaganda, recruit new members, or exert command and control over their networks. Alternatively, threat actors may operate passively, demonstrating operational security awareness online while using their Internet presence to gather information they need to pose an offline physical threat. This paper presents a flexible new prototype system that allows analysts to automatically detect, monitor and characterize threat actors and their networks using publicly available information. The proposed prototype system fills a need in the intelligence community for a capability to automate manual construction and analysis of online threat networks. Leveraging graph sampling approaches, we perform targeted data collection of extremist social media accounts and their networks. We design and incorporate new algorithms for role classification and radicalization detection using insights from social science literature of extremism. Additionally, we develop and implement analytics to facilitate monitoring the dynamic social networks over time. The prototype also incorporates several novel machine learning algorithms for threat actor discovery and characterization, such as classification of user posts into discourse categories, user post summaries and gender prediction.