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 our latest 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 anti-semitism online.
We present an open, scientifically rigorous framework for quantitative analysis of online antisemitism. Our methodology is transparent, and our data will be made available upon request. Using this approach, we characterize the rise of online antisemitism across several axes.
More specifically we answer the following research questions:
Has there been a rise in online antisemitism, and if so, what is the trend?
How is online antisemitism expressed, and how can we automatically discover and categorize newly emerging antisemitic language?
How are memes being weaponized to produce easily digestible and shareable antisemitic ideology?
To what degree are fringe communities influencing the rest of the Web in terms of spreading antisemitic propaganda?
We answer these questions by analyzing a dataset of over 100 million posts from two fringe Web communities: 4chan’s Politically Incorrect board (/pol/) and Gab 1 . We train models, which incorporate continuous bag of words models, using the posts on these Web communities to gain an understanding, and discovery of new antisemitic terms. Our analysis reveals thematic communities of derogatory slang words, nationalistic slurs, and religious hatred toward Jews. We analyze almost seven million images using an image processing pipeline we previously developed to quantify the prevalence and diversity of the notoriously antisemitic Happy Merchant meme.
We find that the Happy Merchant enjoys substantial popularity in both communities, and its usage overlaps with other general purpose (i.e. not intrinsically antisemitic) memes. Finally, we model the relative influence of several fringe and mainstream communities with respect to dissemination of the Happy Merchant meme.
The next several posts will highlight these findings.