# Controversy and Sentiment in Online News

arXiv: https://arxiv.org/abs/1409.8152 · HTML: https://mlsanity.com/paper/1409.8152/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2014.

## Built on by (most cited first, 6 of 6)

- [Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control](https://mlsanity.com/paper/2108.13556/) (2021)
- [Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit](https://mlsanity.com/paper/2212.00339/) (2022)
- [Structure and dynamics of growing networks of Reddit threads](https://mlsanity.com/paper/2409.04085/) (2024)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)
- [ControversialQA: Exploring Controversy in Question Answering](https://mlsanity.com/paper/2302.05061/) (2023)
- [Exploring Linguistic Style Matching in Online Communities: The Role of Social Context and Conversation Dynamics](https://mlsanity.com/paper/2307.02758/) (2023)

## Abstract (arXiv)

How do news sources tackle controversial issues? In this work, we take a data-driven approach to understand how controversy interplays with emotional expression and biased language in the news. We begin by introducing a new dataset of controversial and non-controversial terms collected using crowdsourcing. Then, focusing on 15 major U.S. news outlets, we compare millions of articles discussing controversial and non-controversial issues over a span of 7 months. We find that in general, when it comes to controversial issues, the use of negative affect and biased language is prevalent, while the use of strong emotion is tempered. We also observe many differences across news sources. Using these findings, we show that we can indicate to what extent an issue is controversial, by comparing it with other issues in terms of how they are portrayed across different media.