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In this paper, I analyze how the so called ‘alt-right’ is using Twitter to mainstream its politics. Understanding alt-right mainstreaming is important because the movement has embraced a Gramscian view of politics that believes cultural change (e.g. normalizing unpopular ideas) must precede institutional change (e.g. fielding candidates for office). To conduct my analysis I created a sample of 1,000 tweets from six ‘alt-right’ leaders. I then asked two questions about the tweets in this sample: what topics did alt-right leaders talk about most frequently, and how did they talk about them. My findings suggest that the ‘alt-right’s’ mainstreaming efforts on Twitter involve a mix of techniques. In terms of race, the alt-right is trying to normalize white identity politics. However, the movement is turning away from blatant misogyny on Twitter, instead strategically mimicking conservative tropes about women needing male protection. Finally, the alt-right references Europe more frequently than the US, suggesting that Europe is a geographic anchor in alt-right discourse.

Online extremism has changed, and censorship by content removal or account suspension alone cannot counteract it. We need a radical new response, argues Louis Reynolds.

Many cases of radicalism, especially violent radicalism, manifest within the context of inter-ethnic conflict and war. Research in this domain has significantly contributed to our overarching comprehension of the issue. This form of radicalism is inherently linked to the dynamics of group affiliation. However, our focus here is directed towards the individual motivation driving radical actions. While radical actions may encompass dynamics of group affiliation (e.g., the most radical pro-life activists being members of the Army of God), they also possess a substantial individual component. For instance, numerous attacks on abortion doctors or clinics are categorized as ’lone-wolf terrorism’.

In this study, we sought to examine whether state-of-the-art Machine Learning tech- nology for text analysis could effectively discern the intent behind radical actions from user-generated content. We analyzed a diverse array of radical texts published online by various activists, including anarchists, Iranian guerrilla members, Indian revolution- aries, activists from the US civil rights movement, and The Army of God. Our analysis reveals that Machine Learning models can successfully identify potential signals indica- tive of the author’s intent behind radical actions.

VOX-Pol Blog post.

This report focuses on the interactional dynamics between anti-Muslim extremists and radical Islamists in Germany and beyond. It reveals ideological underpinnings, approaches to mobilization and communication patterns, which all prove to be analogous on both sides, and it places emphasis on the reciprocity of hate that may serve to intensify processes of individual and group radicalization. Our study presents the first systematic analysis of the interplay between both forms of extremism that plays out on different places on the Internet. It provides direct evidence showing that Islamist and far-right movements converge at different levels and mutually amplify one another. The analysis focuses on measuring the online interaction between extremist content, individuals and events.
Overall, over 10,000 Islamist and far-right Facebook posts and over one million German anti-Muslim tweets between 1 January 2013 and 30 November 2017 were analyzed for this study. Additionally, we conducted three months of ethnographic research into encrypted pro-IS and pro-Al-Qaeda groups on Telegram as well as into far-right chat groups.

This study explores the utility of a sociological model of social organization developed by Best and Luckenbill (1994) to classify the radicalization processes of terrorists (i.e., extremist perpetrators who engaged in ideologically motivated acts of violence) who are usually categorized as loner or lone wolf attackers. There are several organizational frameworks used to define or classify violent acts performed by individuals who may or may not have ties to extremist groups, but these studies largely ignore the role of social relationships in radicalization and the extent to which they inform our knowledge of terror. To address this gap, we apply the Best and Luckenbill model of social organization using a qualitative analysis of three case studies of four lone actor or small cell terrorists. The findings demonstrate lone actors are not always true loners in the context of radicalization, and highlights the ways that the Internet and social ties foster the radicalization processes of terror.