# Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter

arXiv: https://arxiv.org/abs/2103.06960 · HTML: https://mlsanity.com/paper/2103.06960/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ](https://mlsanity.com/paper/1703.00565/) (2017)
- [FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding](https://mlsanity.com/paper/2002.08608/) (2020)
- [An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web](https://mlsanity.com/paper/2008.09961/) (2020)
- [Science vs Conspiracy: collective narratives in the age of (mis)information](https://mlsanity.com/paper/1408.1667/) (2014)
- [Polarization of the Vaccination Debate on Facebook](https://mlsanity.com/paper/1801.02903/) (2018)
- [Political Framing: US COVID19 Blame Game](https://mlsanity.com/paper/2007.09655/) (2020)

## Built on by (most cited first, 7 of 7)

- [Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision](https://mlsanity.com/paper/2210.10031/) (2022)
- [Discovering collective narratives shifts in online discussions](https://mlsanity.com/paper/2307.08541/) (2023)
- [Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media](https://mlsanity.com/paper/2401.10030/) (2024)
- [FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines](https://mlsanity.com/paper/2312.08995/) (2023)
- [Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case Study](https://mlsanity.com/paper/2502.02451/) (2025)
- [Design and analysis of tweet-based election models for the 2021 Mexican legislative election](https://mlsanity.com/paper/2301.00626/) (2023)
- [Automated Identification of Competing Narratives in Political Discourse on Social Media](https://mlsanity.com/paper/2609.11202/) (2026)

## Abstract (arXiv)

The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from elite politicians in the U.S., we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.