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Understanding the causes of interstate conflict continues to be a primary goal of the field of international relations. To that end, scholars continue to rely on large datasets of conflict in the international system. This paper introduces the latest iteration in the most widely used dataset on interstate conflicts, the Militarized Interstate Dispute (MID) 4 data. In this paper we first outline the updated data-collection process for the MID4 data. Second, we present some minor changes and clarifications to the coding rules for the MID4 datasets, as well as pointing out how the MID coding procedures affect several notable "close call" cases. Third, we introduce updates to the existing MID datasets for the years 2002–2010 and provide descriptive statistics that allow comparisons of the newer MID data to prior versions. We also offer some best practices and point out several ways in which the new MID data can contribute to research in international conflict.

Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-nresearch initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents a particularly attractive methodology for accomplishing this task. It is efficient, widely applicable to a variety of data collection efforts, and considerably flexible in tailoring its application for specific research needs. This article offers a holistic review of the application of automated document classification for data collection in political science research by discussing the process in its entirety. We argue that the application of a two-stage support vector machine (SVM) classification process offers advantages over other well-known alternatives, due to the nature of SVMs being a discriminative classifier and having the ability to effectively address two primary attributes of textual data: high dimensionality and extreme sparseness. Evidence for this claim is presented through a discussion of the efficiency gains derived from using automated document classification on the Militarized Interstate Dispute 4 (MID4) data collection project.