Detecting hate crimes through machine learning and natural language processing

Misidentification and misreporting of hate crimes by victims and law enforcement are significant barriers to accurate data collection of hate crimes, and their consequent study and prevention. The use of machine learning in crime detection can improve the accuracy and speed at which reported incidents with bias elements are identified. This study develops a machine learning classifier that categorizes police reports as either events with bias elements or events with no bias elements. We use incident/offense reports from the Seattle Police Department to train a Natural Language Processing classification algorithm. We collect narratives, location data, and victim and suspect demographics to use as features. We evaluate the performance of logistic regression, random forest, and XGBoost algorithms, as well as several text embedding techniques. Despite substantial class imbalance, our model achieves a macro F1-score of 0.79, demonstrating the benefits of applied machine learning in accurately detecting and reporting hate crimes.