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

## TL;DR

This study analyzes race, ethnicity, and national origin-based discrimination on social media and its correlation with hate crimes across 100 U.S. cities, using a novel spatial classification approach.

## Contribution

It introduces a spatially-diverse dataset and classification pipeline to identify targeted discrimination on social media across different regions.

## Key findings

- Targeted discrimination correlates with hate crime rates.
- Linguistic features differ between discrimination and non-discrimination Tweets.
- Discrimination patterns vary geographically and by user behavior.

## Abstract

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.