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In this study, we examine key psychological dimensions in the manifestos authored by the perpetrators of the Christchurch and Utøya massacres, the right-wing extremists Brenton Tarrant and Anders Breivik, and the ISIS propaganda magazine Rumiyah. All texts were authored and disseminated virtually with the purpose of attracting or consolidating support, and justifying violent, discriminatory actions. While right-wing supremacist and extremist Islamist discourses are ostensibly ideologically opposed, previous research has posited the existence of ideational and emotive commonalities. We approach this from a corpus-linguistic perspective, and employ the software LIWC2015 and Wmatrix to explore the dominant psychological dimensions, semantic categories and keywords in these texts. We identify elements that contribute to the construction of a narrative of hate, peril and urgency, and discuss differences in the imagery used to construct these meanings and to appeal to different audiences. Whilst our analysis supports the existence of commonalities in ideological content and discursive strategies, our results identify differences in the target of hate in right-wing supremacist discourse and we differentiate between primarily Islamophobic and racist motives. Finally, we also discuss the limitations inherent in employing these software tools to analyse discourse in the Web 2.0 era.

This article seeks to propose a valuable frame for understanding which processes, either cognitive or practical, take part in the making of a terrorist act within the frame of the onlife region. A term recently coined, onlife, refers to the interacting/indistinguishable domain of online and offline world: this is the environment where today’s global terrorism/extremism flourishes. This region of complexity and the phenomena that entails can valuably explored through the General System Theory (GST). In the first part of the paper how global terrorism is changing, moving into cyber terrorism, at the light of the GST, according to recent theoretical achievements in the field is described in detail. The second part will explore a case study of onlife terrorism perpetrated in New Zealand, in order to focus the passage from the ‘possible’ idea of making a massacre to its effective performance, from onlife radicalization to the onlife event (a livestreamed attack filmed by the author) – through a deliberate use of the internet. At this point, the author will be able to come back to the question posed before, sketching some insights that might be valuable for cyber terrorism as well as for GST, especially for what concerns systemic processes within the moral domain.

Over the past few years, new ideological movements like the Alt-Right have captured the attention and concern of both mainstream media, policy makers, and scholars alike. Today, the methods by which right-wing extremists are radicalized are increasingly taking place within social media platforms and online communities. However, no research has yet investigated methods for proactively detecting online communities that may be displaying overall warning signs of mass ongoing ideological and political radicalization. In our work, we use a variety of text analysis methods to investigate the behavioral patterns of a radical right-wing community on Reddit (r/altright) over a 6-month period until right before it was banned for violation of Reddit terms of service. We find that this community showed aggregated behavioral patterns that aligned with past literature on warning behaviors of individual extremists in online environments, and that these behavioral patterns were not seen in a comparison group of eight other online political communities, similar in size and user engagement. Our research helps build upon the established literature on the detection of extremism in online environments, and has implications for proactive monitoring of online communities.

In the space of 36 minutes on March 15, 2019, it is alleged that Brenton Tarrant, an Australian far-right extremist, fatally shot 51 people in two mosques in Christchurch in the deadliest terrorist attack in New Zealand’s history. What was unique about Tarrant’s attack—at least insofar as extreme-right terrorism is concerned—is that he livestreamed his atrocity on Facebook and in doing so, highlighted the Achilles heel of such platforms when faced with the viral dissemination of extremely violent content.

The rise of the alt-right as a potent and sometimes violent political force has been well documented. Yet the journey of an individual towards upholding these ideologies is less well understood. Alt-righters are not instantly converted, but rather incrementally nudged along a particular medial pathway. Drawing on video testimonies, chat logs, and other studies, this paper explores the interaction between this alt-right “pipeline” and the psyche of a user. It suggests three overlapping cognitive phases that occur within this journey: normalization, acclimation, and dehumanization. Finally, the article examines the individual who has reached the end of this journey, an extremist who nevertheless remains largely unregistered within traditional terrorist classifications.

Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech. As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets collected from Gab and Reddit. These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk Workers. In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.