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arXiv · 2104.09656

"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 \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.

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Alexander Spangher, Nanyun Peng, Jonathan May, Emilio Ferrara. 2021-04-19. "Don't quote me on that": Finding Mixtures of Sources in News Articles. https://arxiv.org/abs/2104.09656

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