  Measuring the intensity of events is crucial for monitoring and tracking
armed conflict. Advances in automated event extraction have yielded massive
data sets of "who did what to whom" micro-records that enable data-driven
approaches to monitoring conflict. The Goldstein scale is a widely-used
expert-based measure that scores events on a conflictual-cooperative scale. It
is 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 an
event's "intensity". This paper takes a latent variable-based approach to
measuring conflict intensity. We introduce a probabilistic generative model
that assumes each observed event is associated with a latent intensity class. A
novel aspect of this model is that it imposes an ordering on the classes, such
that higher-valued classes denote higher levels of intensity. The ordinal
nature of the latent variable is induced from naturally ordered aspects of the
data (e.g., casualty counts) where higher values naturally indicate higher
intensity. We evaluate the proposed model both intrinsically and extrinsically,
showing that it obtains comparatively good held-out predictive performance.
