  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.
