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VOX-Pol Blog post.

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.

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.

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.

As demonstrated by the 2016 presidential election, memes have become the suasory tactic par excellence for the promotional and recruitment efforts of the Alt-right. Memes are not simply humorous shorthands or pithy assertions, but play a significant role in the machinations of politics and how the public comes to understand and respond to their government and compatriots. Using the tools of rhetorical criticism, the authors detail how memetic persuasion operates, with a particular focus on the 2016 election of Donald J. Trump. Make America Meme Again reveals the rhetorical principles used to design Alt-right memes, outlining the myriad ways memes lure mainstream audiences to a number of extremist claims. In particular, this book argues that Alt-right memes impact the culture of digital boards and broader public culture by stultifying discourse, thereby shaping how publics congeal. The authors demonstrate that memes are a mechanism that proliferate white nationalism and exclusionary politics by spreading algorithmically through network cultures in ways that are often difficult to discern. Alt-right memes thus present a significant threat to democratic praxis, one that can begin to be combatted through a rigorous rhetorical analysis of their power and influence. Make America Meme Again illuminates the function of networked persuasion for scholars and practitioners of rhetoric, media, and communication; political theorists; digital humanists; and anyone who has ever seen, crafted, or proliferated a meme.