American Hate Crime Trends Prediction with Event Extraction
This work addresses the need for timely predictions of hate crime trends for law enforcement and policymakers, though it is incremental as it builds on existing text mining and prediction techniques.
The paper tackles the problem of predicting national and state-level hate crime trends by extracting events from New York Times news, showing that this method significantly enhances prediction performance compared to time series or regression methods without event-related factors.
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.