American Hate Crime Trends Prediction with Event Extraction

TL;DR

This paper introduces a framework that extracts hate crime events from news articles to improve the prediction of national and state-level hate crime trends in the US, addressing delays in official reporting.

Contribution

It presents a novel event extraction-based approach for hate crime trend prediction, enhancing accuracy over traditional time series and regression methods.

Findings

Event extraction improves trend prediction accuracy

Framework outperforms traditional time series models

Broadens methods for hate crime trend analysis

Abstract

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.