  Online hate speech is responsible for violent attacks such as, e.g., the
Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to
vulnerable groups and society in general. However, little is known about what
makes hate speech on social media go viral. In this paper, we collect N =
25,219 cascades with 65,946 retweets from X (formerly known as Twitter) and
classify them as hateful vs. normal. Using a generalized linear regression, we
then estimate differences in the spread of hateful vs. normal content based on
author and content variables. We thereby identify important determinants that
explain differences in the spreading of hateful vs. normal content. For
example, hateful content authored by verified users is disproportionally more
likely to go viral than hateful content from non-verified ones: hateful content
from a verified user (as opposed to normal content) has a 3.5 times larger
cascade size, a 3.2 times longer cascade lifetime, and a 1.2 times larger
structural virality. Altogether, we offer novel insights into the virality of
hate speech on social media.
