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Military intelligence is underutilized in the study of civil war violence.Declassified records are hard to acquire and difficult to explore with thestandard econometrics toolbox. I investigate a contemporary government databaseof civilians targeted during the Vietnam War. The data are detailed, with up to45 attributes recorded for 73,712 individual civilian suspects. I employ anunsupervised machine learning approach of cleaning, variable selection,dimensionality reduction, and clustering. I find support for a simplifyingtypology of civilian targeting that distinguishes different kinds of suspectsand different kinds targeting methods. The typology is robust, successfullyclustering both government actors and rebel departments into groups that mirrortheir known functions. The exercise highlights methods for dealing with highdimensional found conflict data. It also illustrates how aggregating measuresof political violence masks a complex underlying empirical data generatingprocess as well as a complex institutional reporting process.

Understanding Civil War Violence through Military Intelligence: Mining Civilian Targeting Records from the Vietnam War