  We study malicious online content via a specific type of hate speech: race,
ethnicity and national-origin based discrimination in social media, alongside
hate crimes motivated by those characteristics, in 100 cities across the United
States. We develop a spatially-diverse training dataset and classification
pipeline to delineate targeted and self-narration of discrimination on social
media, accounting for language across geographies. Controlling for census
parameters, we find that the proportion of discrimination that is targeted is
associated with the number of hate crimes. Finally, we explore the linguistic
features of discrimination Tweets in relation to hate crimes by city, features
used by users who Tweet different amounts of discrimination, and features of
discrimination compared to non-discrimination Tweets. Findings from this
spatial study can inform future studies of how discrimination in physical and
virtual worlds vary by place, or how physical and virtual world discrimination
may synergize.
