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The recent rise of the political extremism in Western countries has spurred renewed interest in the psychological and moral appeal of political extremism. Empirical support for the psychological explanation using surveys has been limited by lack of access to extremist groups, while field studies have missed psychological measures and failed to compare extremists with contrast groups. We revisit the debate over the psychological and moral appeal of extremism in the U.S. context by analyzing Twitter data of 10,000 political extremists and comparing their text-based psychological constructs with those of 5000 liberal and 5000 conservative users. The results reveal that extremists show a lower positive emotion and a higher negative emotion than partisan users, but their differences in certainty is not significant. In addition, while left-wing extremists express more language indicative of anxiety than liberals, right-wing extremists express lower anxiety than conservatives. Moreover, our results mostly lend support to Moral Foundations Theory for partisan users and extend it to the political extremists. With the exception of ingroup loyalty, we found evidences supporting the Moral Foundations Theory among left- and right-wing extremists. However, we found no evidence for elevated moral foundations among political extremists.

In the 1990s and early 2000s, there was much discussion about the democratic and anti-democratic implications of the Internet. The latter particularly focused on the ways in which the far-right were using the Internet to spread hate and recruit members. Despite this common assumption, the American far-right did not harness the Internet quickly, effectively or widely. More recently, however, they have experienced a resurgence and mainstreaming, benefitting greatly from social media. This chapter examines the history of their use of the Internet with respect to: (1) how this developed in response to political changes and emerging technologies; (2) how it reflected and changed the status of such movements and their brand of hate; and (3) the relationship between online activity and traditional methods of communication.

Previously theorised as vehicles for expressing progressive dissent, this article considers how political memes have become entangled in the recent reactionary turn of web subcultures. Drawing on Chantal Mouffe’s work on political affect, this article examines how online anonymous communities use memetic literacy, memetic abstraction, and memetic antagonism to constitute themselves as political collectives. Specifically, it focuses on how the subcultural and highly reactionary milieu of 4chan’s /pol/ board does so through an anti-Semitic meme called triple parentheses. In aggregating the contents of this peculiar meme from a large dataset of /pol/ comments, the article finds that /pol/ users, or anons, tend to use the meme to formulate a nebulous out-group resonant with populist demagoguery.

This chapter scrutinises how it is that anonymous chan culture engage with the alt-histories of esoteric fascism. To this end, the chapter begins with a brief description of the ideas of the mid-century Italian esotericist ‘Baron’ Julius Evola (arguably the single most significant intellectual influence esoteric fascism), before moving on to discuss anon’s vernacular interpretation of these ideas. In order to assess the ‘real world’ danger posed by these alt-histories, the essay concludes with a brief analysis of the particularly disturbing case of the Christchurch mass shooter whose manifesto was replete with anonymous chan culture and whom appears to have imagined himself as a holy warrior engaged fighting against ‘the great replacement’ of Western culture by alien outsiders—a notion which connects extreme-right terrorists with the discourse of new-right populist politicians and intellectuals.

With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to pro-actively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been traditionally studied for structured datasets, but we aim at bias mitigation from unstructured text data. In this paper, we make two important contributions. First, we systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes. Second, we propose novel methods leveraging knowledge-based generalizations for bias-free learning. Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance. We experiment with multiple knowledge generalization policies and analyze their effect on general performance and in mitigating bias. Our experiments with two real-world datasets, a Wikipedia Talk Pages dataset (WikiDetox) of size ~ 96k and a Twitter dataset of size ~ 24k, show that the use of knowledge-based generalizations results in better performance by forcing the classifier to learn from generalized content. Our methods utilize existing knowledge-bases and can easily be extended to other tasks.

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.