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

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