Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes
Aida Mostafazadeh Davani | Leigh Yeh | Mohammad Atari | Brendan Kennedy | Gwenyth Portillo Wightman | Elaine Gonzalez | Natalie Delong | Rhea Bhatia | Arineh Mirinjian | Xiang Ren | Morteza Dehghani
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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.