"Don't quote me on that": Finding Mixtures of Sources in News Articles
Alexander Spangher, Nanyun Peng, Jonathan May, Emilio Ferrara
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Abstract
Journalists publish statements provided by people, or 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 affiliation and 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 computational journalism systems.