  This paper investigates how hate speech varies in systematic ways according
to the identities it targets. Across multiple hate speech datasets annotated
for targeted identities, we find that classifiers trained on hate speech
targeting specific identity groups struggle to generalize to other targeted
identities. This provides empirical evidence for differences in hate speech by
target identity; we then investigate which patterns structure this variation.
We find that the targeted demographic category (e.g. gender/sexuality or
race/ethnicity) appears to have a greater effect on the language of hate speech
than does the relative social power of the targeted identity group. We also
find that words associated with hate speech targeting specific identities often
relate to stereotypes, histories of oppression, current social movements, and
other social contexts specific to identities. These experiments suggest the
importance of considering targeted identity, as well as the social contexts
associated with these identities, in automated hate speech classification.
