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

arXiv: https://arxiv.org/abs/2104.09656 · HTML: https://mlsanity.com/paper/2104.09656/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories](https://mlsanity.com/paper/1904.04164/) (2019)
- [Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines](https://mlsanity.com/paper/1705.09656/) (2017)

## Built on by (most cited first, 5 of 5)

- [NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge](https://mlsanity.com/paper/2206.07106/) (2022)
- [Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus](https://mlsanity.com/paper/2207.03592/) (2022)
- [Sequentially Controlled Text Generation](https://mlsanity.com/paper/2301.02299/) (2023)
- [Identifying Informational Sources in News Articles](https://mlsanity.com/paper/2305.14904/) (2023)
- [Explaining Mixtures of Sources in News Articles](https://mlsanity.com/paper/2411.05192/) (2024)

## Abstract (arXiv)

Journalists publish statements provided by people, or textitsources 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 textitaffiliation and textitrole. 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 textitcomputational journalism systems.