# Controversy in Context

arXiv: https://arxiv.org/abs/1908.07491 · HTML: https://mlsanity.com/paper/1908.07491/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues](https://mlsanity.com/paper/2310.18130/) (2023)
- [Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL](https://mlsanity.com/paper/2503.03654/) (2025)
- [Controversy Detection: a Text and Graph Neural Network Based Approach](https://mlsanity.com/paper/2112.11445/) (2021)

## Abstract (arXiv)

With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is. Prior works relied on Wikipedia's metadata and on content analysis of the articles pertaining to a concept in question. Here we show that the immediate textual context of a concept is strongly indicative of this property, and, using simple and language-independent machine-learning tools, we leverage this observation to achieve state-of-the-art results in controversiality prediction. In addition, we analyze and make available a new dataset of concepts labeled for controversiality. It is significantly larger than existing datasets, and grades concepts on a 0-10 scale, rather than treating controversiality as a binary label.