# American Hate Crime Trends Prediction with Event Extraction

arXiv: https://arxiv.org/abs/2111.04951 · HTML: https://mlsanity.com/paper/2111.04951/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [Analyzing the Targets of Hate in Online Social Media](https://mlsanity.com/paper/1603.07709/) (2016)
- [Giveme5W1H: A Universal System for Extracting Main Events from News Articles](https://mlsanity.com/paper/1909.02766/) (2019)
- [Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes](https://mlsanity.com/paper/1909.02126/) (2019)
- [From Fear to Hate: How the Covid-19 Pandemic Sparks Racial Animus in the United States](https://mlsanity.com/paper/2007.01448/) (2020)

## Built on by (most cited first, 2 of 2)

- [Syntactic-GCN Bert based Chinese Event Extraction](https://mlsanity.com/paper/2112.09939/) (2021)
- [Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior](https://mlsanity.com/paper/2507.20614/) (2025)

## Abstract (arXiv)

Social media platforms may provide potential space for discourses that contain hate speech, and even worse, can act as a propagation mechanism for hate crimes. The FBI's Uniform Crime Reporting (UCR) Program collects hate crime data and releases statistic report yearly. These statistics provide information in determining national hate crime trends. The statistics can also provide valuable holistic and strategic insight for law enforcement agencies or justify lawmakers for specific legislation. However, the reports are mostly released next year and lag behind many immediate needs. Recent research mainly focuses on hate speech detection in social media text or empirical studies on the impact of a confirmed crime. This paper proposes a framework that first utilizes text mining techniques to extract hate crime events from New York Times news, then uses the results to facilitate predicting American national-level and state-level hate crime trends. Experimental results show that our method can significantly enhance the prediction performance compared with time series or regression methods without event-related factors. Our framework broadens the methods of national-level and state-level hate crime trends prediction.