An Automated Information Extraction Tool for International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design

Abstract

Despite widespread recognition that aggregated summary statistics on international conflict and cooperation miss most of the complex interactions among nations, the vast majority of scholars continue to employ annual, quarterly, or (occasionally) monthly observations. Daily events data, coded from some of the huge volume of news stories produced by journalists, have not been used much for the past two decades. We offer some reason to change this practice, which we feel should lead to considerably increased use of these data. We address advances in event categorization schemes and software programs that automatically produce data by “reading” news stories without human coders. We design a method that makes it feasible, for the first time, to evaluate these programs when they are applied in areas with the particular characteristics of international conflict and cooperation data, namely event categories with highly unequal prevalences, and where rare events (such as highly conflictual actions) are of special interest. We use this rare events design to evaluate one existing program, and find it to be as good as trained human coders, but obviously far less expensive to use. For large-scale data collections, the program dominates human coding. Our new evaluative method should be of use in international relations, as well as more generally in the field of computational linguistics, for evaluating other automated information extraction tools. We believe that the data created by programs similar to the one we evaluated should see dramatically increased use in international relations research. To facilitate this process, we are releasing with this article data on 3.7 million international events, covering the entire world for the past decade. ^a

Footnotes

^a Thanks to Marianne Abbott, Doug Bond, Joe Bond, Gerard Bradford, Carl Cobb, John Freeman, Joshua Goldstein, Patricia Hastings, Craig Jenkins, Dylan Balch-Lindsay, Churl Oh, Jon Pevehouse, Kevin Quinn, Phil Schrodt, and Langche Zeng for helpful discussions; Valerie Abejuro, Sam Cook, Dan Epstein, Andrew Holbrook, Shane Jensen, Orit Kedar, Adrian Ma, Amit Pathare, and Mikhail Pryadilnikov for research assistance; and the National Science Foundation (IIS-9874747), the National Institutes of Aging (P01 AG17625-01), the Weatherhead Center for International Affairs, and the World Health Organization for research support. The data described in this article are available at http://GKing.Harvard.edu.