Geolocating Political Events in Text
This provides a general method for political scientists to geolocate events from text, enabling applications like analyzing civilian casualties in conflicts such as the Syrian civil war.
The paper tackles the problem of automatically linking political events in text to their geographic locations, achieving human-level performance and outperforming previous event geolocation systems.
This work introduces a general method for automatically finding the locations where political events in text occurred. Using a novel set of 8,000 labeled sentences, I create a method to link automatically extracted events and locations in text. The model achieves human level performance on the annotation task and outperforms previous event geolocation systems. It can be applied to most event extraction systems across geographic contexts. I formalize the event--location linking task, describe the neural network model, describe the potential uses of such a system in political science, and demonstrate a workflow to answer an open question on the role of conventional military offensives in causing civilian casualties in the Syrian civil war.