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Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada

Zachary Yang, Anne Imouza, Maximilian Puelma Touzel, Cecile Amadoro, Gabrielle Desrosiers-Brisebois, Kellin Pelrine, Sacha Levy, Jean-Francois Godbout, Reihaneh Rabbany

TL;DR

This study develops a geography- and time-resolved framework to measure partisan polarization in COVID-19 discourse on Twitter in the US and Canada, using topic-conditioned language dissimilarity and a $C$-index-based polarization metric. It combines RoBERTa-based embeddings, geo-located user data, and party-affiliation labeling to quantify polarization across three interventions (lockdowns, masks, vaccines) and over time, enabling event-driven analysis. Key findings show higher polarization in conservative regions, a strong US-specific link between vaccine-polarization and vaccination rates, and rapid, short-lived polarization peaks tied to political and health events, with Canada displaying weaker but still regionally structured polarization. The methodology provides fine-grained insights for monitoring polarizing discussions and informing public health communication strategies during pandemics and beyond.

Abstract

Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.

Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada

TL;DR

This study develops a geography- and time-resolved framework to measure partisan polarization in COVID-19 discourse on Twitter in the US and Canada, using topic-conditioned language dissimilarity and a -index-based polarization metric. It combines RoBERTa-based embeddings, geo-located user data, and party-affiliation labeling to quantify polarization across three interventions (lockdowns, masks, vaccines) and over time, enabling event-driven analysis. Key findings show higher polarization in conservative regions, a strong US-specific link between vaccine-polarization and vaccination rates, and rapid, short-lived polarization peaks tied to political and health events, with Canada displaying weaker but still regionally structured polarization. The methodology provides fine-grained insights for monitoring polarizing discussions and informing public health communication strategies during pandemics and beyond.

Abstract

Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.
Paper Structure (41 sections, 2 equations, 21 figures, 10 tables, 1 algorithm)

This paper contains 41 sections, 2 equations, 21 figures, 10 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Approach
  3. Application to late 2020 pandemic discourse in the United States & Canada
  4. Results
  5. Regional Variation in Partisan Polarization
  6. Temporal Variation in Partisan Polarization
  7. United States Polarization Timecourse
  8. Canadian Polarization Timecourse
  9. Aggregate Polarization
  10. The Relationship between Conspiracy and Polarization
  11. Discussion
  12. United States
  13. Canada
  14. Limitations
  15. Conclusion
  16. ...and 26 more sections

Figures (21)

  • Figure 1: Overview of the proposed method to estimate partisan polarization over date, region, and topic (top), as well as how to analyze this data by collapsing it over any of those three dimensions (bottom). We studied the topics of lockdowns, masks, and vaccines.
  • Figure 2: Regional distribution of partisan polarization in the United States on three key topics of Lockdown (a), Mask (b), and Vaccines (c). Color intensity from light to dark gives the amount of polarization measured weekly between October 11, 2020 to January 3, 2021 and then averaged over the 12 weeks. We also report the average weekly percentage of conspiracy-related tweets that are posted from users in each region in panel (d).
  • Figure 3: Regional distribution of partisan polarization in Canada on three key topics of Lockdowns (a), Masks (b), and Vaccines (c). The polarization is measured weekly between October 11, 2020 to January 3, 2021 and the averaged over 12 weeks is used for this plot. We also report the average weekly percentage of conspiracy-related tweets that are posted from users in each region (d). Provinces and territory boundaries are colored based on the number of users we had in our data from those regions, which indicates the support for our measurement: Light-grey for less than 100 users, grey for between 100 and 1,000 users and black for greater than 1,000 users.
  • Figure 4: Ranking of American states partisan polarization per topic and overall. Ranking of 1 signifies the highest average weekly polarization between October 11, 2020 to January 3, 2021 (12 weeks). State names are colored based on the vote margin for the conservative party from the 2020 United States Presidential Election (Conservative Party: Republican Party; Liberal Party: Democratic Party).
  • Figure 5: Partisan polarization ranking of Canadian provinces and territories per topic and overall. A ranking of 1 signifies the highest average weekly polarization between October 11, 2020 to January 3, 2021 (12 weeks). Province or territory names are colored (red to blue) based on the vote margin for the conservative party family from Canada’s 2019 Federal Election (Liberal Party Family: Liberal, New Democratic Party, Green; Conservative Party Family: Conservative, People’s Party). Line colors have a transparency to reflect the support for the measurement, based on the number of users in that region.
  • ...and 16 more figures