"Don't quote me on that": Finding Mixtures of Sources in News Articles

Journalists publish statements provided by people, or $\mathit{\text{sources}}$ 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 $\mathit{\text{affiliation}}$ and $\mathit{\text{role}}$. 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.