# Quantifying Polarization on Twitter: the Kavanaugh Nomination

arXiv: https://arxiv.org/abs/2001.02125 · HTML: https://mlsanity.com/paper/2001.02125/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2020.

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

- [CommunityLM: Probing Partisan Worldviews from Language Models](https://mlsanity.com/paper/2209.07065/) (2022)
- [Fringe News Networks: Dynamics of US News Viewership following the 2020 Presidential Election](https://mlsanity.com/paper/2101.10112/) (2021)
- [Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction](https://mlsanity.com/paper/2603.23531/) (2026)

## Abstract (arXiv)

This paper addresses polarization quantification, particularly as it pertains to the nomination of Brett Kavanaugh to the US Supreme Court and his subsequent confirmation with the narrowest margin since 1881. Republican (GOP) and Democratic (DNC) senators voted overwhelmingly along party lines. In this paper, we examine political polarization concerning the nomination among Twitter users. To do so, we accurately identify the stance of more than 128 thousand Twitter users towards Kavanaugh's nomination using both semi-supervised and supervised classification. Next, we quantify the polarization between the different groups in terms of who they retweet and which hashtags they use. We modify existing polarization quantification measures to make them more efficient and more effective. We also characterize the polarization between users who supported and opposed the nomination.