  Terrorism is a major problem worldwide, causing thousands of fatalities and
billions of dollars in damage every year. Toward the end of better
understanding and mitigating these attacks, we present a set of machine
learning models that learn from localized news data in order to predict whether
a terrorist attack will occur on a given calendar date and in a given state.
The best model--a Random Forest that learns from a novel variable-length moving
average representation of the feature space--achieves area under the receiver
operating characteristic scores $> .667$ on four of the five states that were
impacted most by terrorism between 2015 and 2018. Our key findings include that
modeling terrorism as a set of independent events, rather than as a continuous
process, is a fruitful approach--especially when the events are sparse and
dissimilar. Additionally, our results highlight the need for localized models
that account for differences between locations. From a machine learning
perspective, we found that the Random Forest model outperformed several deep
models on our multimodal, noisy, and imbalanced data set, thus demonstrating
the efficacy of our novel feature representation method in such a context. We
also show that its predictions are relatively robust to time gaps between
attacks and observed characteristics of the attacks. Finally, we analyze
factors that limit model performance, which include a noisy feature space and
small amount of available data. These contributions provide an important
foundation for the use of machine learning in efforts against terrorism in the
United States and beyond.
