Quantifying Polarization on Twitter: the Kavanaugh Nomination

TL;DR

This paper analyzes political polarization on Twitter during Brett Kavanaugh's Supreme Court nomination by classifying user stances and quantifying polarization through retweets and hashtags, improving existing measures.

Contribution

It introduces a semi-supervised and supervised method for stance detection and enhances polarization quantification measures for Twitter data.

Findings

Identified stance of over 128,000 Twitter users.

Quantified polarization based on retweets and hashtags.

Improved efficiency and effectiveness of polarization measures.

Abstract

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.