  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 these
troubling trends. In this paper, we present a large-scale, quantitative study
of online antisemitism. We collect hundreds of million posts and images from
alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and
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. 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 communities like Twitter and Reddit. Taken together, our results
provide a data-driven, quantitative framework for understanding online
antisemitism. Our methods serve as a framework to augment current qualitative
efforts by anti-hate groups, providing new insights into the growth and spread
of hate online.
