  Journalists publish statements provided by people, or \textit{sources} to
contextualize current events, help voters make informed decisions, and hold
powerful individuals accountable. In this work, we construct an ontological
labeling system for sources based on each source's \textit{affiliation} and
\textit{role}. We build a probabilistic model to infer these attributes for
named sources and to describe news articles as mixtures of these sources. Our
model outperforms existing mixture modeling and co-clustering approaches and
correctly infers source-type in 80\% of expert-evaluated trials. Such work can
facilitate research in downstream tasks like opinion and argumentation mining,
representing a first step towards machine-in-the-loop \textit{computational
journalism} systems.
