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

arXiv: https://arxiv.org/abs/1902.00119 · HTML: https://mlsanity.com/paper/1902.00119/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [DISARM: Detecting the Victims Targeted by Harmful Memes](https://mlsanity.com/paper/2205.05738/) (2022)
- [Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?](https://mlsanity.com/paper/2301.11219/) (2023)
- [Sexism Identification in Tweets and Gabs using Deep Neural Networks](https://mlsanity.com/paper/2111.03612/) (2021)
- [SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations](https://mlsanity.com/paper/2305.14728/) (2023)
- [Decoding Memes: Benchmarking Narrative Role Classification across Multilingual and Multimodal Models](https://mlsanity.com/paper/2506.23122/) (2025)

## Abstract (arXiv)

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.