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Cited by 1Social Media and PoliticsMisinformation and Its ImpactsCommunication and COVID-19 Impact

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Regional and temporal patterns of partisan polarization during the COVID-19 pandemic in the United States and Canada

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Yang, Zachary, Imouza, Anne, Puelma Touzel, Maximilian, Amadoro, Cécile, Desrosiers-Brisebois, Gabrielle, Pelrine, Kellin, Levy, Sacha, Godbout, Jean-François, Rabbany, Reihaneh·2026·PLOS One
DOI 10.1371/journal.pone.0347327PMC PMC13095112
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Abstract

During the COVID-19 pandemic, public health measures emerged as highly polarizing topics in online discourse, with debates intensifying around specific policy implementations and events. This study introduces a novel computational approach to measure subnational and event-driven variation in partisan polarization and explores these dynamics across the United States and Canada. Analyzing over 50 million tweets from late 2020—a critical period of polarizing discourse during the pandemic’s early phase—we examine regional variations in discussions surrounding three key health interventions: lockdowns, masks, and vaccines. Our analysis reveals that politically conservative regions exhibited significantly higher levels of partisan polarization in both countries, with this effect particularly pronounced in the United States. We demonstrate a strong negative correlation between regional vaccination rates and the degree of polarization in vaccine-related discussions in the U.S., suggesting tangible public health implications of online partisan division. This relationship was notably weaker in Canada, pointing to important cross-national differences in how political polarization manifests and impacts health behaviors. By tracking the temporal evolution of polarization, we identify distinct spikes linked to specific political events and policy announcements. These polarization surges typically lasted only a few days, revealing the dynamic nature of online partisan discourse. The geographic heterogeneity in polarization patterns—with certain conservative states showing unexpectedly low polarization and some liberal states displaying high polarization—highlights the complex interplay between political ideology, policy implementation, and public response during health crises. While our polarization index reflects the discourse of politically engaged Twitter users, it nevertheless captures key dynamics of online debate that influence public narratives and align with observable regional outcomes. Our findings suggest that online discussions both reflect and potentially drive rapid changes in public opinion, with measurable consequences for regional public health outcomes. This computational framework for quantifying polarization provides a valuable tool for researchers and policymakers to understand, monitor, and potentially address partisan divisions during public health emergencies and beyond.

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Fig 1 Overview of estimating partisan polarization over date, region and topic. Using a predefined set of political keywords, we extract and embed tweets, geolocate users, and classify them by party affiliation. Tweets are then categorized by key topic (lockdowns, masks, and vaccines) allowing us to measure partisan polarization over time (daily) and across regions (weekly) in both Canada and the U.S.
Fig 1 Overview of estimating partisan polarization over date, region and topic. Using a predefined set of political keywords, we extract and embed tweets, geolocate users, and classify them by party affiliation. Tweets are then categorized by key topic (lockdowns, masks, and vaccines) allowing us to measure partisan polarization over time (daily) and across regions (weekly) in both Canada and the U.S.CC BY · PubMed Central
Fig 2 Normalized distribution of the inferred user party affiliations compared to the Canadian 2021 election results. A: Canadian 2021 election results. B: Geolocated users. C: Lockdown. D: Mask. E: Vaccine. F: Conspiracy.
Fig 2 Normalized distribution of the inferred user party affiliations compared to the Canadian 2021 election results. A: Canadian 2021 election results. B: Geolocated users. C: Lockdown. D: Mask. E: Vaccine. F: Conspiracy.CC BY · PubMed Central
Fig 3 Empirical distribution of the inferred user party affiliations compared to the US 2020 election results. Interestingly, lockdown matches closer to the election results, mask and vaccine has a higher liberal ratio and conspiracy has a higher conservative ratio. A: Canadian 2021 election results. B: Geolocated users. C: Lockdown. D: Mask. E: Vaccine. F: Conspiracy.
Fig 3 Empirical distribution of the inferred user party affiliations compared to the US 2020 election results. Interestingly, lockdown matches closer to the election results, mask and vaccine has a higher liberal ratio and conspiracy has a higher conservative ratio. A: Canadian 2021 election results. B: Geolocated users. C: Lockdown. D: Mask. E: Vaccine. F: Conspiracy.CC BY · PubMed Central
Fig 4 Approximation of poli: Error and Runtime Tradeoff. A: Approximation quality. For large enough data, the approximation error is close to 0.001.B: Time saved in seconds. Our approximation of polarization also exponentially saves time as the number of instances grows. For both panels, we plot the mean and standard deviation over 10 independent runs across different minimum coefficients of variation (CV).
Fig 4 Approximation of poli: Error and Runtime Tradeoff. A: Approximation quality. For large enough data, the approximation error is close to 0.001.B: Time saved in seconds. Our approximation of polarization also exponentially saves time as the number of instances grows. For both panels, we plot the mean and standard deviation over 10 independent runs across different minimum coefficients of variation (CV).CC BY · PubMed Central
Fig 5 Approximation of poli: Effect of Minimum Coefficient of Variation. Green dots mean time was saved while red means time lost. The circle size is the absolute error times 1000. The inner plot zooms in and doubles the circle sizes for clarity. We observe that the fraction of instances needed increase as we decrease the needed minimum coefficient of variation. This follows our intuition as a larger sample of data is much easier to approximate the full data. We also that at lower minimum coefficient of variation, the less error we have (circles are much smaller). On all plots, we also see that we always save time (green circles) if there are more than 10,000 instances.
Fig 5 Approximation of poli: Effect of Minimum Coefficient of Variation. Green dots mean time was saved while red means time lost. The circle size is the absolute error times 1000. The inner plot zooms in and doubles the circle sizes for clarity. We observe that the fraction of instances needed increase as we decrease the needed minimum coefficient of variation. This follows our intuition as a larger sample of data is much easier to approximate the full data. We also that at lower minimum coefficient of variation, the less error we have (circles are much smaller). On all plots, we also see that we always save time (green circles) if there are more than 10,000 instances.CC BY · PubMed Central
Fig 6 Approximation of poli: Effect of Repeat Count. As in the preceding figure, we observe that as we increase the number of repeats, the less error we have in our approximation (smaller circles). Likewise the amount of time saved decreases as we increase the repeat count (amount of red circles increase as repeats increase). We also observe a general slight increase in the fraction of instances needed as we increase the repeats.
Fig 6 Approximation of poli: Effect of Repeat Count. As in the preceding figure, we observe that as we increase the number of repeats, the less error we have in our approximation (smaller circles). Likewise the amount of time saved decreases as we increase the repeat count (amount of red circles increase as repeats increase). We also observe a general slight increase in the fraction of instances needed as we increase the repeats.CC BY · PubMed Central
Fig 7 Regional distribution of partisan polarization in the United States. A: Lockdown. B: Mask. C: Vaccine. D: % of conspiracy-related tweets. 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).
Fig 7 Regional distribution of partisan polarization in the United States. A: Lockdown. B: Mask. C: Vaccine. D: % of conspiracy-related tweets. 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).CC BY · PubMed Central
Fig 8 Regional distribution of partisan polarization in Canada. A: Lockdown. B: Mask. C: Vaccine. D: % of conspiracy-related tweets. 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 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.
Fig 8 Regional distribution of partisan polarization in Canada. A: Lockdown. B: Mask. C: Vaccine. D: % of conspiracy-related tweets. 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 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.CC BY · PubMed Central
Fig 9 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 according to the colorbar (red to blue), based on the vote margin for the conservative party from the 2020 United States Presidential Election (Conservative Party: Republican Party; Liberal Party: Democratic Party).
Fig 9 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 according to the colorbar (red to blue), based on the vote margin for the conservative party from the 2020 United States Presidential Election (Conservative Party: Republican Party; Liberal Party: Democratic Party).CC BY · PubMed Central
Fig 10 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.
Fig 10 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.CC BY · PubMed Central
Fig 11 Correlation between polarization score and vote margin for the conservative party. Colors (blue to red) are conservative party vote margin (same as Fig 9 and Fig 10). Significant correlation between Polarization Score and Vote Margin is found for the US discourse on masks and on vaccines for which the respective Pearson r correlation and p-value is shown.
Fig 11 Correlation between polarization score and vote margin for the conservative party. Colors (blue to red) are conservative party vote margin (same as Fig 9 and Fig 10). Significant correlation between Polarization Score and Vote Margin is found for the US discourse on masks and on vaccines for which the respective Pearson r correlation and p-value is shown.CC BY · PubMed Central
Fig 12 Relation between vaccines polarization and vaccination rates in the United States. Color (blue to red) is again the respective conservative party vote margin from the 2020 U.S. Presidential Election. The correlation is −0.77 with CI = [−0.86, −0.62] (n = 51, p = 6.97e-11).
Fig 12 Relation between vaccines polarization and vaccination rates in the United States. Color (blue to red) is again the respective conservative party vote margin from the 2020 U.S. Presidential Election. The correlation is −0.77 with CI = [−0.86, −0.62] (n = 51, p = 6.97e-11).CC BY · PubMed Central
Fig 13 Daily trends of partisan polarization in the United States and Canada from October 9, 2020 to January 3, 2021. A: US Daily Lockdown Polarization. B: Canadian Daily Lockdown Polarization. C: US Daily Mask Polarization. D: Canadian Daily Lockdown Polarization. E: US Daily Vaccine Polarization. F: Canadian Daily Vaccine Polarization. The vertical dashed lines denote pre-selected political and vaccine-related events as explained in the text. In addition to the polarization measure (purple line), we also report the tweet volume, in log-scale, on the corresponding topic (yellow line) per day which denotes the size of support for our measurement.
Fig 13 Daily trends of partisan polarization in the United States and Canada from October 9, 2020 to January 3, 2021. A: US Daily Lockdown Polarization. B: Canadian Daily Lockdown Polarization. C: US Daily Mask Polarization. D: Canadian Daily Lockdown Polarization. E: US Daily Vaccine Polarization. F: Canadian Daily Vaccine Polarization. The vertical dashed lines denote pre-selected political and vaccine-related events as explained in the text. In addition to the polarization measure (purple line), we also report the tweet volume, in log-scale, on the corresponding topic (yellow line) per day which denotes the size of support for our measurement.CC BY · PubMed Central
Fig 14 Daily aggregated partisan polarization. For the U.S. (A, B) and Canada (C, D), polarization is aggregated over topic by averaging over the values. We show pandemic-related new cases and deaths in background for reference. Event-triggered average polarization for identified events are listed in Table 9 and Table 10 respectively. Shaded region denotes standard deviation over the 5 events for each country.
Fig 14 Daily aggregated partisan polarization. For the U.S. (A, B) and Canada (C, D), polarization is aggregated over topic by averaging over the values. We show pandemic-related new cases and deaths in background for reference. Event-triggered average polarization for identified events are listed in Table 9 and Table 10 respectively. Shaded region denotes standard deviation over the 5 events for each country.CC BY · PubMed Central
Fig 15 Relation between the volume of conspiracy related content and the observed partisan polarization in the United States and Canada. In the left column, we report the volume of conspiracy related tweets posted by users for the United States (A: affiliated with the Democrat and Republican party) and Canada (C: affiliated with the liberal (left) Party Family (LPF)—Liberal, New Democratic Party, Green, and the conservative (right) Party Family (RPF)—Conservative, People’s Party for Canada). On the right, we show the relation between daily partisan polarization summed over the different topics and the overall volume of conspiracy tweets. In the United States (B), we find there is a statistically significant correlation −0.247 with CI=[−0.448,-0.023] (n = 88, p = 0.031) between these measures. In Canada (D), we find that there is no statistically significant correlation of 0.023 with CI=[−0.187,0.231] (n = 88, p = 0.831) between these measures.
Fig 15 Relation between the volume of conspiracy related content and the observed partisan polarization in the United States and Canada. In the left column, we report the volume of conspiracy related tweets posted by users for the United States (A: affiliated with the Democrat and Republican party) and Canada (C: affiliated with the liberal (left) Party Family (LPF)—Liberal, New Democratic Party, Green, and the conservative (right) Party Family (RPF)—Conservative, People’s Party for Canada). On the right, we show the relation between daily partisan polarization summed over the different topics and the overall volume of conspiracy tweets. In the United States (B), we find there is a statistically significant correlation −0.247 with CI=[−0.448,-0.023] (n = 88, p = 0.031) between these measures. In Canada (D), we find that there is no statistically significant correlation of 0.023 with CI=[−0.187,0.231] (n = 88, p = 0.831) between these measures.CC BY · PubMed Central

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Metadata

Authors
Yang, Z., Imouza, A., Puelma Touzel, M., Amadoro, C., Desrosiers-Brisebois, G., Pelrine, K., Levy, S., Godbout, J.-F., & Rabbany, R.
Affiliations
  • Mila - Quebec Artificial Intelligence Institute (CA)
  • McGill University (CA)
Corresponding author
Yang, Zachary
ORCID
Venue
PLOS One
Publisher
Public Library of Science (PLoS)
Published
2026-04-20
Volume
21
Issue
4
Pages
e0347327
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0347327
PMC
PMC13095112
OpenAlex
W4400375943
Cited by
1
References
0
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • CIFAR AI Catalyst Grant (Being politic smart in the age of misinformation)
  • CIFAR AI Research Chair
Topics
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Communication › Communication and COVID-19 Impact
Keywords
partisan polarization, COVID-19 pandemic, Twitter discourse, public health policy debate, lockdown, face masks, COVID-19 vaccines, regional variation, vaccination coverage, cross-national comparison, United States, Canada, online political discourse
MeSH terms
COVID-19, SARS-CoV-2, Canada, Humans, Politics, Public Health, United States, Pandemics, Social Media

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Regional and temporal patterns of partisan polarization during the COVID-19 pandemic in the United States and Canada. https://app.cochat.ai/papers/10.1371/journal.pone.0347327
The paper
Yang, Z., Imouza, A., Puelma Touzel, M., Amadoro, C., Desrosiers-Brisebois, G., Pelrine, K., Levy, S., Godbout, J.-F., & Rabbany, R. (2026). Regional and temporal patterns of partisan polarization during the COVID-19 pandemic in the United States and Canada. PLOS One. https://doi.org/10.1371/journal.pone.0347327