# Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes

arXiv: https://arxiv.org/abs/1909.02126 · HTML: https://mlsanity.com/paper/1909.02126/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

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

- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction](https://mlsanity.com/paper/2205.00498/) (2022)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [American Hate Crime Trends Prediction with Event Extraction](https://mlsanity.com/paper/2111.04951/) (2021)

## Abstract (arXiv)

Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations, there are no official reports from a large number of US cities regarding incidents of hate. Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of local news articles, can be used to predict instances of hate crime. We then use the trained model to detect incidents of hate in cities for which the FBI lacks statistics. Lastly, we train models on predicting homicide and kidnapping, compare the predictions to FBI reports, and establish that incidents of hate are indeed under-reported, compared to other types of crimes, in local press.