This project will collect, process, and analyze millions of U.S. government records concerning international relations, develop tools to explore these records, and make all of them available on a single website with an Application Programming Interface. The project will demonstrate how computational techniques can aid both qualitative and quantitative social science research on a range of areas of major public interest, expanding knowledge about terrorism, intelligence, international trade and aid. Among its broader impacts, it will improve the infrastructure available for multidisciplinary research and teaching, and also give citizens, journalists, and civil society organizations much better access to information about international relations. Participating student researchers will both learn about -- and contribute to -- practical methods to keep government transparent and accountable in the age of "big data."
The exponential growth in digitized or "born digital" documents will make such methods increasingly important in years to come. To meet the challenge, the team will draw on new work in Natural Language Processing, customize existing tools that turn text into data, and develop new tools for use with historical documents. They will employ Named Entity Recognition techniques to extract names of people, countries, and organizations, enabling users to track the absolute and relative frequency of mentions in large digital archives. Through Topic Modeling, they will summarize the thematic content and show how the most important subjects change over time. And Social Network extraction will allow them to reveal the informal relationships that shape policy from day-to-day. By bringing together all of this quantitative declassified data in a single platform, the project will advance work by political scientists as well as scholars of communications and social networks who seek to understand agenda-setting, power, and influence within and between organizations. The platform will enable researchers to test fundamental questions in IR theory, such as whether policymakers generally speak to one another in terms of "state security," as realists insist, or "international norms," as liberalism assumes. It will allow users to shift between different levels of analysis, from an aggregate view of whole archives, to filtered subsets of metadata, to the specific words in one sentence that produced a single data point. It will bring together quantitative and qualitative approaches to research in international relations, and make both more transparent, rigorous, and replicable.
