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We study malicious online content via a specific type of hate speech: race,ethnicity and national-origin based discrimination in social media, alongsidehate crimes motivated by those characteristics, in 100 cities across the UnitedStates. We develop a spatially-diverse training dataset and classificationpipeline to delineate targeted and self-narration of discrimination on socialmedia, accounting for language across geographies. Controlling for censusparameters, we find that the proportion of discrimination that is targeted isassociated with the number of hate crimes. Finally, we explore the linguisticfeatures of discrimination Tweets in relation to hate crimes by city, featuresused by users who Tweet different amounts of discrimination, and features ofdiscrimination compared to non-discrimination Tweets. Findings from thisspatial study can inform future studies of how discrimination in physical andvirtual worlds vary by place, or how physical and virtual world discriminationmay synergize.

Race, Ethnicity and National Origin-based Discrimination in Social Media and Hate Crimes Across 100 U.S. Cities