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Since the inception of the World Wide Web, security agencies, researchers, and analysts have focused much of their attention on the sentiment found on hate-inspired web-forums. Here, one of their goals has been to detect and measure users’ affects that are expressed in the forums as well as identify how users’ affects change over time. Manual inspection has been one way to do this; however, as the number of discussion posts and sub-forums increase, there has been a growing need for an automated system that can assist humans in their analysis. The aim of this paper, then, is to detect and measure a number of affects expressed in written text on Stormfront.org, the most visited hate forum on the Web. To do this, we used a machine learning approach where we trained a model to recognize affects on three sub-forums: Ideology and Philosophy, For Stormfront Ladies Only, and Stormfront Ireland. The training data consisted of manual annotated data and the affects we focused on were racism, aggression, and worries. Results indicate that even though measuring affects is a subjective process, machine learning is a promising way forward to analyze and measure the presence of different affects on hate forums.

Our primary research question was whether memes and codewords, private or fringe online forums, and hybrid real-world/online militia—the three characteristic tactics that support outbreaks of extremist violence for both Jihadi and Boogaloo extremism—are also prevalent in anti-fascist and anarcho-socialist groups. To analyze the use and prevalence of memes and other coded language and activity, we performed analyses on over ten million social media comments ranging from mainstream platforms (such as Twitter) to fringe online forums on Reddit. Throughout the research, we examined whether the same extremist themes and actions that are characteristic of both Jihadi and Boogaloo extremists—themes such as violent revolution, martyrdom, and having a utopian narrative, and actions such as terror attacks—are also found in extremist groups espousing anti-fascism and anarcho-socialism.

A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand this worrying trend. In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million comments and images from alt-right Web communities like 4chan’s Politically Incorrect board (/pol/) and the Twitter clone, Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the “Unite the Right” rally in Charlottesville. Furthermore, this antisemitism appears in tandem with sharp increases in white ethnic nationalist content on the same communities. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic “Happy Merchant” meme, and in particular how these fringe communities influence its propagation to more mainstream services like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our open and scientifically grounded methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of antisemitism online.

At the dawn of mass access to the internet, Douglas Rushkof wrote Cyberia: Life in the Trenches of Hyperspace. In his book, he observed a very special moment in our recent history in which it was possible to imagine the path ahead, before most of what daily users of the internet now
experience even existed.

VOX-Pol Blog post.

Online disruption efforts generally aim to reduce the availability of jihadist content. Yet, the speed and agility of jihadist movements online – a multiplatform approach which a co-author of this paper has previously described as a ‘swarmcast’ – has allowed groups to evolve in response to disruption efforts and find new ways to distribute content. This paper describes a model of the flow of users between social media platforms and surface web pages to access jihadist content, using data obtained through innovative collection methods. The model provides an approximate picture of the jihadist information ecosystem and how multiple platforms are used to disseminate content.