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Measuring the intensity of events is crucial for monitoring and trackingarmed conflict. Advances in automated event extraction have yielded massivedata sets of "who did what to whom" micro-records that enable data-drivenapproaches to monitoring conflict. The Goldstein scale is a widely-usedexpert-based measure that scores events on a conflictual-cooperative scale. Itis based only on the action category ("what") and disregards the subject("who") and object ("to whom") of an event, as well as contextual information,like associated casualty count, that should contribute to the perception of anevent's "intensity". This paper takes a latent variable-based approach tomeasuring conflict intensity. We introduce a probabilistic generative modelthat assumes each observed event is associated with a latent intensity class. Anovel aspect of this model is that it imposes an ordering on the classes, suchthat higher-valued classes denote higher levels of intensity. The ordinalnature of the latent variable is induced from naturally ordered aspects of thedata (e.g., casualty counts) where higher values naturally indicate higherintensity. We evaluate the proposed model both intrinsically and extrinsically,showing that it obtains comparatively good held-out predictive performance.

An Ordinal Latent Variable Model of Conflict Intensity