[1,3]Zachary Yang

1]School of Computer Science, McGill University

2]Political Science, McGill University

3]Montreal Institute for Learning Algorithms

4]Department of Political Science, University of Montreal

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

<EMAIL_ADDRESS>Anne Imouza  Maximilian Puelma Touzel  Cécile
Amadoro  Gabrielle Desrosiers-Brisebois  Kellin Pelrine  Sacha Levy  Jean-
François Godbout  Reihaneh Rabbany  [  [  [  [

###### 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.

###### keywords:

Polarization, COVID-19, Social Media, Computational Social Science

##  1 Introduction

Partisan polarization is increasingly prevalent in democracies around the
world [1]. In the United States, the level of opposition between Democrats and
Republicans has been steadily growing for decades [2, 3] and reached
unprecedented heights during the 2020 presidential election [1, 4]. This
polarization even affected how individuals responded to the COVID-19 pandemic
by influencing their assessment of the dangers posed by the virus and their
response to public health measures [5, 6, 7]. Several studies have now
confirmed that supporters of the Democratic party were more likely to follow
social distancing measures [8, 9, 10], wear masks [11, 12], and get vaccinated
[13, 14, 15] when compared to their Republican counterparts. This polarizing
trend among partisans is not limited to the United States [16, 17]. Other
countries, like Canada, have also experienced the rapid politicization of
pandemic responses, where researchers found that supporters of the Liberal
Party were more likely to follow COVID-19 guidelines than supporters of the
Conservative Party or the populist People’s Party [18, 19].

While countries like the United States and Canada adopted strategies to
coordinate efforts in addressing the pandemic at the national level, there was
still significant variation in both the amount and types of public health
interventions introduced by subnational governments. For instance, Canadian
provinces implemented different policies, ranging from comprehensive lockdowns
and school closures to more targeted guidelines focusing on specific
populations [20]. Similar variation can be observed in the United States,
where some states implemented strict lockdown orders and mask mandates, while
others refused to limit social distancing [21]. Much like at the national
level, these different regional policies also became rapidly politicized along
partisan lines [17], especially on social media platforms, where the
politicization of the COVID-19 pandemic largely unfolded [22, 23, 10, 24].

Numerous studies have now confirmed that online discussions surrounding the
pandemic [25, 9, 26, 27] exhibited clear regional patterns characterized by
the same partisan animosity that impacted the heterogeneous implementation of
public health measures [25] and the resulting epidemiological outcomes [9].
Additional research highlights that these partisan divisions also contributed
to the increasing polarization observed on social media [25, 27, 24]. The
intense reactions from both supporters and opponents of public health measures
[28] implies that public opinion could have significantly been influenced by
local political dynamics and the geography of the pandemic [29]. This regional
heterogeneity provides us with a unique opportunity to study polarization
around specific events, topics, and regions, to understand how various factors
affected compliance to COVID-19 guidelines. However, reliably measuring the
polarization of public discourse at more fine-grained resolution is a
challenge, even with the large quantity of human-generated text with extensive
meta-data available on digital platforms.

In this work, we propose a solution to this measurement problem by introducing
a comprehensive approach to better understand the geographic and event-driven
variation of online partisan polarization of COVID-19 discussions within
American states and Canadian provinces. We examine variations in public
discourse as contained on Twitter (now X) to determine how polarization is
related to: (1) the ideological leanings of different regions; (2) the amount
of conspiracy theory-related messages that users have been exposed to; and (3)
vaccination data.

The paper is organized as follows. First, we briefly outline our approach to
region- and time-resolved polarization data from Twitter (X). In this section,
we also describe our machine learning method to classify users as conservative
or liberal and justify our choice of topic-conditioned language dissimilarity
as a proxy for partisan polarization. Next, we present the results of applying
our approach to a large-scale dataset we collected in 2020, filtered through
three prominent pandemic-related public health interventions: lockdowns,
masks, and vaccines. Our findings indicate that conservative regions in both
countries exhibited higher polarization levels on these topics overall. We
also find strong negative correlation between vaccination rates in different
U.S. regions and the level of polarization in their online discussions related
to vaccines. We close with a discussion of limitations of the approach and
promising new areas of application.

##  2 Approach

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.

We developed a method to measure geographically-resolved partisan polarization
over time from large-scale social media message datasets (see fig. 1). The
language of political discussions across socio-demographic groups can vary
significantly [30], each having their own lexicon, so dissimilar language on
its own does not imply polarized positions. However, when conditioning on
discussion of the same, contentious topic, the dissimilarity of the language
used by different demographics is more likely to reflect alternative semantic
understandings of that specific topic, which we assume are highly correlated
with polarization. This correlation is weakened by linguistic differences not
captured by the particular definition of semantic separation used, so the
latter is only a noisy proxy of polarization strength. That said, we expect
that a stronger correlation between semantic separation and polarization
exists when language is represented in a more expressive model. Inspired by
the demonstrated capacity of modern vector embeddings to represent the
semantics of words, our approach focuses on transforming the sentences of
social media posts using RoBERTa, a powerful open-source, language-embedding
model [31]. As an indicator for polarization, we then measure dissimilarity by
how far apart the tweets of left and right-leaning users are in this embedding
space. In particular, we use the C-index [32], a robust clustering measure
based on the average of pairwise distances of embedded partisan users within a
partisan group relative to the average of the largest and smallest distances
overall. To label partisans in our data, we developed and validated a machine
learning method that identifies users as conservative or liberal on the
ideological spectrum by cross-indexing multiple metadata sources. We also
developed and validated a method to geolocate users to resolve polarization’s
geographic heterogeneity. Details of these components of the approach can be
found in the Methods section.

###  2.1 Application to late 2020 pandemic discourse in the United States &
Canada

We collected a large-scale dataset of COVID-19 political discussions on
Twitter (X) occurring between October 9, 2020, to January 4, 2021, comprising
about 46.6 million tweets linked to Canada and 12.5 million tweets linked to
the United States. We geolocated users based on their provided location and
classified them by their declared party affiliation. Specifically, we include
identifiers for the two major liberal and conservative ideological divisions
in each country: the left (Liberal Party, New Democratic Party, and Green
Party) and the right (Conservative Party and People’s Party) for Canada; and
the Democratic (left) and Republican (right) parties for the United States.
Using official population census and election results, we verify that these
data provide a politically balanced set of users in the different regions of
these two countries. For each of the users, we then compute a vector
representation for the language they used in their social media messages
during this period. We condition on region, time, and topic and for each
combination compute the value of the C-index as a proxy for polarization. To
narrow the content of the messages analyzed, we focus on three specific topics
of discussion: lockdowns, masks and vaccines. These topics were chosen because
of their salience for polarized discourse around the pandemic [27, 33, 34],
and because they span different types of interventions (group behaviour,
individual behaviour, and medicine, respectively). Based on these
measurements, we then compare the polarization observed in different American
states and Canadian provinces over time for each of the three topics. We also
look into how polarization is correlated with epidemiological data and
conspiracy-related content. We refer the reader to the Methods section for
further details.

##  3 Results

We organize the presentation of results as follows. First, we report the
geographical trends of the observed partisan polarization in the United States
and Canada and confirm that conservative states and provinces display more
polarized online discourse. Next, we highlight the correlation between
partisan polarization on the topic of vaccines and the vaccination rates found
across U.S. states. We then present our event-based analysis of the temporal
patterns of polarization at the national level in both countries and report
correlations between polarization and vaccination data, as well as the volume
of conspiracy-related content on Twitter (X). Finally, we examine the
different peaks in polarization and explain how they relate to various
polarizing events.

(a) Lockdown Polarization

(b) Mask Polarization

(c) Vaccine Polarization

(d) % of Conspiracy-related Tweets

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).

(a) Lockdowns Polarization

(b) Masks Polarization

(c) Vaccines Polarization

(d) % of Conspiracy-related Tweets

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.

###  3.1 Regional Variation in Partisan Polarization

Our analysis begins by visualizing the geography of partisan polarization in
fig. 2 and fig. 3 for the United States and Canada. The regional heterogeneity
in the amount of polarization observed over different topics is apparent in
both countries. We also see heterogeneity in the amount of conspiracy-related
tweets shown in fig. 2d and fig. 3d for both countries, respectively.

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.

(a) Correlation between Polarization Score and Vote Margin for Conservative
Party. Colors (blue to red) are conservative party vote margin (same as fig. 4
and fig. 5). 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.

(b) 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).

Figure 6:

Next, we analyze how this heterogeneity varies with the partisan leanings
found in each region by analyzing election voting patterns (the 2020
presidential election in the case of the United States and the 2019 federal
election for Canada). Our first observation is that conservative states and
provinces show higher levels of polarization compared to their liberal
counterparts. To display results over all covered regions, we show the
polarization rankings for American states and Canadian provinces in fig. 4 and
fig. 5, respectively. This ranking is applied separately to each of the three
topics and is based on their weekly polarization averaged over the 12-week
period. An additional fourth ranking labelled overall is shown and gives the
average over the three topics. Each region is associated with a color graded
from blue to red based on the vote margin for the Republican party (US) or the
conservative party family (Canada) obtained from the votes reported in the
most recent election in their corresponding country. In these figures, a blue
to red color gradient for conservative to progressive is used such that the
names of predominantly conservative/Republican regions appear in red,
predominantly liberal/Democratic regions in blue, and mixed or less definitive
regions in purple. Referring to fig. 4, in the United States we can see
clearly that conservative states are more polarized compared to liberal states
overall and specifically on discussions related to masks and vaccines. The
ranking is more mixed in the discussions about lockdown measures with outliers
from both liberal and conservative states; namely Idaho, Alabama, and Arkansas
showing the least polarization, and Delaware (ranked 1st), Colorado (ranked
17th), and New Jersey (ranked 15th) showing higher values. The expected
relationship between pandemic response and state partisanship is however still
present for lockdown discussions, with liberal states such as Vermont (ranked
41st) and Massachusetts (ranked 43rd) displaying less polarization compared to
more conservative states such as Mississippi (ranked 2nd), North Dakota
(ranked 3rd), and Oklahoma (ranked 4th).

Looking now at Canada fig. 4, we find that Alberta, a conservative province,
shows higher polarization compared to Ontario, and British Columbia (among the
Canadian provinces with the most number of social media users). Quebec is
overall the highest ranked province. Although the pandemic was highly
polarized in Quebec—e.g., with violent protests [35]—we want to acknowledge
the limitation of our study, which was focused on the English language; in
Quebec, the main language is French whereas only English tweets were included
in our analysis.

Finally, the correlation between polarization and the partisan vote margin is
more clearly represented in the scatter between the two, shown for both
countries in fig. 6(a). We see a strong and significant correlation between
Republican vote share in the United States and the polarization index around
masks and vaccines discourse, but not lockdowns. The remaining associations
(Lockdown for US, and all topics for Canada) are, however, insignificant (for
Canada this is in part due to the relatively small number of regions).

The strong correlation that we observe between the polarization score for
discourse around vaccines in conservative-leaning states follows the well-
known negative correlation between Republican vote share and vaccination rates
[36]. Since vaccines were not available yet over this time period, we
nevertheless present a comparison using official vaccination rates measured
for different states by the U.S. Centers for Disease Control and Prevention
for a similar period of time one year later, after the vaccines were rolled
out (i.e. October 11, 2021 to January 3, 2022). Averaging on a weekly basis,
we confirm this correlation in fig. 6(b), where we observe that vaccine
polarization is strongly negatively correlated to vaccination rates in the
different American states. We did not observe a similar pattern in Canada, due
to small sample size and the implementation of vaccine mandates. While
disentangling the causal relationships among conservative vote margin,
polarization score, and vaccination rate is not possible here, the results
suggest that polarized discourse played a role in shaping the highly
heterogeneous vaccination rates across the U.S.

###  3.2 Temporal Variation in Partisan Polarization

We next focus on the temporal trends of daily partisan polarization at the
national level for each topic and overall, as displayed in fig. 7 for the
United States and Canada, respectively. In these figures, the value of the
metric fluctuates rapidly on the timescale of days. This is on the faster end
of the range of timescales found in other topic tracking studies, e.g., [37].
These short timescales are consistent with our assumption that language adapts
quickly in rapid anticipation of or as an immediate response to specific
events. In particular, we considered two kinds of events. First, we
preselected political and vaccine-related events (shown in table 1(a) and as
vertical lines in fig. 7). These provide the scaffold for the socio-political
trajectory of each country related to political discourse and pandemic
response. Second, we detected highly polarized events through analysis of the
highest two peaks in polarization (shown in table 1(b) and as red circles in
fig. 7). We also show in the figures the tweet volume as a relative indicator
of day-by-day reliability of the estimation of the polarization score. While
we do not have direct causal evidence linking a highlighted peak in the
polarization score to a specific event, we do find that many of the largest
polarization peaks occur around highly contentious events related to each
country’s specific context (table 1(b)). In the following two sections, we
summarize these events and discuss how they relate to the topic for which the
polarization simultaneously peaks.

(a) Major political and pandemic-related events in each country such as wheen the FDA (U.S. Food & Drug Administration), and PHAC (Public Health Agency of Canada) approved vaccines.  Date | Event | Country
---|---|---
Nov. 3 | US National Election | US
Oct. 24 | BC General Election | Canada
Oct. 26 | Saskatchewan General Election | Canada
Dec. 8 | States resolve controversies | US
Dec. 9 | PHAC approves Pfizer vaccine | Canada
Dec. 11 | FDA approves vaccines | US
| Electoral votes submitted | US
Dec. 14 | Vaccination begins | Canada
Dec. 20 | Moderna vaccine distributed | US
| Election votes arrive | US
Dec. 23 | PHAC approves Moderna Vaccine | Canada

(b) Polarization peaks and their corresponding events. For each topic, we analyzed the two highest peaks, and inferred the content discussed on those peaks. | United States | Canada
---|---|---
Topic | Date | Polarizing Event | Date | Polarizing Event
| Nov. 1 | Viral tweet by Trump | Oct. 17 | Toronto Mask Measures Protest
Lockdown | Nov. 21 | Unidentified topic | Oct. 29 | Calgary Mask Measures Protest
| Oct. 21 | Viral tweet by Trump | Oct. 12 | Unidentifed topic
Masks | Nov. 14 | Biden proposes mandates | Nov. 14 | PHAC recommends masks
| Dec. 20 | Moderna vaccine distributed | Dec. 20 | Moderna distributed in U.S.
Vaccines | Dec 22 | Biden gets vaccinated | Dec. 23 | PHAC approves Moderna

Table 1: Major dates and peaks within the United States and Canada during
2020.

##### United States Polarization Timecourse

The left column of fig. 7 reports the daily polarization measured for the
three key topics of lockdowns, masks, and vaccines. Peaks in polarization on
the lockdown topic in fig. 7(a) may correspond to partisan differences in
public support (or discontent) and discourse surrounding COVID-19 measures. In
the days leading up to the 2020 Presidential Election on November 3rd, a
pillar of President Trump’s campaign messaging on the pandemic characterized
lockdowns as tyranny and economic repression [38]. For example, on November 1,
2020, the date of the second-largest peak, Trump made a highly controversial
claim by stating that the election was a choice between implementing deadly
lockdown measures supported by Biden or an efficient end to the COVID-19
crisis with a safe vaccine [39]. Trump also made other similar claims on
Twitter (X) during this period, e.g., when he said (sic): “Biden wants to LOCK
DOWN our Country, maybe for years. Crazy! There will be NO LOCKDOWNS. The
great American Comeback is underway!!!” [38].

Next, contentious debates related to masks were found coincident with peaks in
polarization, as shown in fig. 7(c). For example, while we did not find an
event external to social media on October 31, 2020, the date of the highest
peak, we did find that the most retweeted tweet by Democrats on that day was
“RT @JoeBiden: Be a patriot. Wear a mask.”. This, in turn, generated strong
responses that day from Republicans with the third most retweeted tweet within
this group: “RT @RealBrysonGray: There’s literally nothing patriotic about
being so scared of a virus with a 99.9…”. This is then possibly an example of
influencer post-driven, rather than real world event-driven polarization.
Another set of divisive messages were observed on November 14, 2020, the next
highest peak, after presidential candidate Biden proposed mandatory mask
mandates, and South Dakota Governor Kristi Noem announced her opposition to
this measure; nearly half of all the top retweets referred to Noem’s
statement.

Finally, fig. 7(e) reports trends in polarization around the topic of
vaccines. Here, some of the peaks observed are simultaneous with important
events surrounding COVID-19 vaccine efficacy. We see that the second-largest
peak occurred on December 22, 2020, a day after Biden received his first
COVID-19 vaccine shot [40]. The most retweeted tweet for both partisan groups
that day was “RT @JoeBiden: Today, I received the COVID-19 vaccine. To the
scientists and researchers who worked tirelessly to make this possible -
than…”. However, while supporters of Biden congratulated him, by tweeting
messages like “RT @YAFBiden: And just like that, @JoeBiden has received the
COVID-19 vaccine!”, opponents instead promoted pro-Trump messages, e.g. “RT
@TheLeoTerrell: Finally a @JoeBiden confession. He finally gave credit to
@realDonaldTrump and #OperationWarpSpeed. It’s about time.”.

(a) US Daily Lockdown Polarization

(b) Canada Daily Lockdown Polarization

(c) US Daily Mask Polarization

(d) Canada Daily Masks Polarization

(e) US Daily Vaccine Polarization

(f) Canada Daily Vaccine Polarization

Figure 7:  Daily trends of partisan polarization in the United States  and
Canada  from October 9, 2020 to January 3, 2021. 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.

##### Canadian Polarization Timecourse

The right column of fig. 7 shows the daily polarization measured for the three
key topics of lockdowns, masks, and vaccines. The pre-selected events in the
Canadian timeline (table 1(a)) are marked as vertical lines in the figure.

In fig. 7(b), on the topic of lockdowns, we observe the highest peak on
October 17, 2020, which coincides with the Toronto anti-mask protest, a large
demonstration where thousands of protesters rallied against COVID-19 lockdown
measures. The second-highest peak is observed on November 29, 2020, when the
national news reported a Calgary Mask Measures protest on the preceding day
[41].

The highest polarization peak on mask-related tweets is found on November 14,
2020, as seen in fig. 7(d), coincident with the peak in the US plot 7(c)
mentioned earlier. This event was discussed by the conservative-Party family
users, showing how partisan discourse in the U.S. might be driving some
polarization in Canada.

Looking at the polarization of discussions about vaccines in fig. 7(f), we
also observe the highest peaks are in response to key vaccine-related events:
The two highest peaks in polarization observed on December 20 and 23, 2020
coincided with the distribution of the Moderna COVID-19 vaccines in the U.S.
and Health Canada’s approval of the Moderna vaccine [42], respectively. While
the former is an event associated with the United States, it led to
discussions in Canada about vaccine prioritization and availability [43]. On
the 20, top retweets by liberal Party Family users focused on news of the
Republican politicians being first in line for the vaccine, while conservative
Party Family users retweeted more diverse anti-vaccine sentiment. On December
23, 2020, the top retweets were strong sentiments in support of and in
opposition to the approval.

##### Aggregate Polarization

(a)

(b)

(c)

(d)

Figure 8: 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. vent-triggered average polarization for identified events listed in
table 1(b). Shaded region denotes standard deviation over the 5 events for
each country.

To complement the granular analysis presented above, we also evaluated the
measure’s overall responsiveness to polarizing events. In particular, we
computed an average of the polarization score over topics (shown in fig. 8a,b)
and then performed event-triggered averaging around such events, to show how
the metric varies in time before and after these particular dates on average.
This aggregate result (shown in fig. 8c) confirms a fast (on the order of
days) and largely symmetric rise-and-decay profile around these polarization
peaks.

###  3.3 The Relationship between Conspiracy and Polarization

Finally, we explored the relationship between conspiracy discourse and
polarization using our time-resolved measurements of the number of conspiracy-
related tweets. In particular, we compared it with the time course of the
aggregate daily polarization presented in the previous section. The profiles
broken down by progressive and liberal partisanship for U.S. and Canada are
shown in fig. 9(c) and fig. 9(a), respectively. For both countries,
conservative partisans tweet conspiracy-related content in higher numbers than
progressive partisans. The correlation with aggregate daily polarization for
the U.S. and Canada (fig. 8(a) and fig. 8(c)) is shown in fig. 9(b) and fig.
9(d), respectively. For the U.S., we find a small but significant negative
correlation with polarization.

(a)

(b)

(c)

(d)

Figure 9: 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.

##  4 Discussion

This article investigated regional and event-triggered variation in partisan
division within social media debates surrounding the introduction of COVID-19
public health measures across American states and Canadian provinces. Our
computational analysis was centered around quantifying partisan polarization
by analyzing the language used in millions of online messages from users
affiliated with different political parties. In particular, we focused on
Twitter (X) discussions related to three key public health interventions
during the early phases of the COVID-19 pandemic: lockdowns, masks, and
vaccines, as well as tracking the volume of conspiracy-related tweets. Our
analysis explored the geographic heterogeneity of polarization and identified
political events that likely influenced public opinion over time.

Like several other studies before us [e.g., 44, 17, 45, 46], we found that
more right leaning states and provinces exhibited greater partisan divisions
around COVID-19 on Twitter (X), in particular concerning topics of mask
mandates and vaccine distribution. However, we went beyond these studies to
characterize the geographic heterogeneity and time course of polarization,
relating features in our polarization metric to real world events. We looked
into the relationship between polarization and public health initiatives in
the U.S. and confirmed a strong negative correlation between partisan
polarization and future vaccination rates and a moderate negative correlation
between the temporal profiles of volume of conspiracy-related tweets and
aggregate polarization. We did not observe similar patterns in Canada.

###  4.1 United States

The early phases of the COVID-19 pandemic in the United States prompted a
variety of public discussions online that reflected strong regional variation
of partisan support [25, 47, 48, 49, 50]. State-specific polarization obtained
from our computational approach could be expected to be uniformly low, with
the message content of conservative and liberal states each having internally
homogeneous semantics. Instead, our analysis confirmed that polarization was
notably higher in conservative states, where we found that Republican vote
margins had a significant positive effect on polarization in discussions
concerning masks and vaccines, after controlling for other factors.

This main result is consistent with previous studies that suggest conservative
states exhibited higher levels of polarization in response to public health
interventions compared to their liberal counterparts [10, 48]. Nevertheless,
we found that this pattern does not hold across every state. Delaware, a
liberal state, exhibited a distinctly high level of polarization, likely due
to the strict public health measures implemented by the governor in response
to the rapid increase in the number of cases during the first wave of the
pandemic [51, 52, 53]. Similarly, low levels of polarization were observed in
several conservative states, like Arkansas, Alabama, and Idaho, with a
possible explanation coming from more unified opposition to restrictive
COVID-19 measures like mask mandate orders [54]. Nevertheless, most
conservative states exhibited relatively high levels of polarization (fig.
6(a)).

Our analysis did not identify a single, general cause for this relationship.
That said, commonalities in each state’s trajectory in the pandemic offer some
clues: e.g., Mississippi, North Dakota, and Oklahoma experienced specific
political decisions—such as mask mandates—made in the earlier phase of the
pandemic, that led to resistance despite rising COVID-19 cases [55, 56, 47].
In Mississippi, the decision by the governor to lift the statewide mask
mandate in late September could also have contributed to heightened levels of
polarization [55, 54]. A similar pattern was observed in North Dakota, South
Dakota, and Oklahoma, where initial hesitancy to enforce mask mandates also
appears to have led to increased partisan divisions [57, 58, 59, 60, 61, 62,
54]. This finding is not surprising, since the party affiliation of governors
is the most important predictor of the widespread adoption of mask mandates
[63]. One possible explanation for our main result that can be gleaned from
these anecdotes is that pandemic severity increasingly strains the more
uniform opposition to restrictive health measures in more conservative states,
leading them to exhibit higher levels of polarization [64, 65, 54]. This is a
distinct source of polarization than that in states with more equal
distribution of partisans across competitive districts.

Through our approach, we could also dissect how polarization varies in time
over the three topics we considered: lockdowns, masks, and vaccines. These
three topics exhibited similar baseline levels of polarization during the
period of study, which was between the second and third waves of the pandemic,
punctuated by large positive deviations that typically rise and fall quickly.
The prevalence of these deviations was smallest for the lockdown topic. Its
low correlation with Republican party vote share suggests that it did not act
as a meaningful indicator of partisan opposition. The polarization time course
for masks and vaccines, however, contained many, sharp peaks, many of which we
were able to identify with a real world event. For example, South Dakota
initially experienced very high levels of mask polarization, coinciding with
efforts by medical authorities to promote mask-wearing, despite Governor
Kristi Noem’s opposition [54, 66]. Her public display of opposition to Biden’s
suggestion of mask mandates lead to one such peak in polarization. Similarly,
North Dakota also displayed early signs of increased polarization on
conspiracy theories [47], which may have been exacerbated by the posthumous
electoral win of a Republican candidate who died from COVID-19 [67, 68, 69].

The strong negative correlation between vaccines polarization and vaccination
rates that we observed in the U.S. demonstrates that states with higher
vaccination rates were also less polarized around this issue [70]. The exact
origin of this correlation is unclear. However, factors such as education and
political ideology, which also have a strong geographic dependence, likely
played a role [71]. Indeed, higher education levels are generally associated
with greater vaccine acceptance and trust in vaccine safety [72]. Moreover,
Democrats tend to trust the COVID-19 vaccines more and have been early
adopters, whereas Republicans generally show lower levels of such trust [73].

Overall, the high levels of polarization observed in the United States
relative to Canada point to a more divided society. Several studies have
confirmed that conservative states and counties were less likely to adopt
social distancing measures, impose mask mandates, and get vaccinated in the
second and third wave of the pandemic. Our study offers new insights into
these trends by demonstrating that they correlate with regional heterogeneity
in social media discourse, particularly during salient political events around
health measures. We also found that this discourse reflects changes in the
pandemic timeline, initially related to stay-at-home lockdown orders, followed
by mask mandates, and later transitioning to vaccines as they first became
available.

###  4.2 Canada

The Canadian set of results also suggest that partisan divisions influenced
public responses to COVID-19 measures in this country [74]. Compared to the
U.S., we found a similar, albeit much weaker association between polarization
and conservative political leaning, with conservative provinces like Alberta
and Saskatchewan experiencing higher levels of polarization during stricter
lockdown measures than their more liberal counterparts, such as British
Columbia and Ontario [75, 35]. Polarization levels varied over smaller and
medium-sized provinces as well, measured as relatively high for New Brunswick
and low for Nunavut, where the rates of COVID-19 infections remained
relatively low during the pandemic (the sole COVID-free jurisdiction in North
America until November 2020) [76, 77]. Additionally, we also found that
polarization surrounding mask mandates and vaccines were not homogeneously
distributed across provinces.

Among the Canadian provinces, Quebec is an interesting case for our analysis
of polarization. For example, our results confirmed that Quebec had the
highest level of partisan division over vaccines, but also the highest
reported incidence of COVID-19 in Canada during the first and second waves of
the pandemic [78]. Quebec’s unique approach to managing the pandemic with its
more restrictive measures relative to other provinces is also somewhat
reflected in our results. After a relative hiatus with several restrictions
relaxed in the summer of 2020, Quebec once again became the epicenter of the
pandemic in the fall [79]. This resurgence led to the reinstatement of strict
pandemic control measures and a ban on public demonstrations following
significant anti-government protests against lockdowns and mask mandates [80,
81]. These events also coincided with an increase in online conflicts,
promoted by Canadian far-right populist rhetoric and conspiracy theories on
Twitter (X) [82]. It is important to note, however, that most of these
conversations in Canada were heavily influenced by discussions in the US, with
Canadians retweeting American vaccine-related content 8 times as often as
Canadian content during the period covered by our study [83, 84]. Likewise,
vaccine hesitancy was also linked to political affiliation in Canada, with
those supporting the Conservative Party more likely to refuse vaccination
[85].

As in the U.S. case, the polarization time course computed for Canada also
exhibits spikes observed around key events like protests against lockdown
measures, mask mandates, and vaccine roll-outs. These findings suggest that
public reactions to significant political and social events during the
pandemic are reflected in the measure of polarization we use. We did not
observe the negative correlation between polarization and volume of
conspiracy-related tweets that we saw in the U.S. case. This contrasts with
[83], who found a reduction in negative sentiment in Canadian vaccine-related
tweets between January and December 2020. The relationship between
polarization and sentiment is complex and long-term trends are likely driven
by processes besides pure volume of discussion around conspiracy theories
[86].

Finally, the relatively lower influence of polarization on vaccine attitudes
may be attributed to the country’s more widespread vaccine mandates [87, 88].
This prevalence, along with higher levels of trust in politicians [89] and
social capital [90, 91], could have contributed to a broader acceptance of
COVID-19 health interventions [84, 85]. Indeed, there was a rare ‘cross-
partisan consensus’ among Canadians regarding emergency measures in the early
stages of the pandemic [92]. This consensus, however, was not mirrored on
social media, where conspiracy theories widely circulated [24, 84]. Overall,
our results indicate that online discussions surrounding lockdowns, masks, and
vaccines did mirror polarization, and were shaped by regional reactions to
events and circumstances specific to Canadian provinces.

###  4.3 Limitations

While our method offers valuable insights, it comes with certain limitations.
First, we viewed partisan polarization only through the proxy of semantic
similarity. This choice may in certain cases obscure some signals not captured
by the semantic embedding representation. Second, specifically in the Canadian
context, we categorized users into liberal (left) and conservative (right)
party family groups. During the manual annotation of Twitter (X) profiles, we
encountered few users who identified as supporters of the Bloc Quebecois
political party; therefore, we opted to exclude them from the analysis.
Additionally, our classification of users into liberal and conservative
partisan groups is based on self-reported information, which may not be
entirely accurate. Third, it is important to note that our analysis is based
on Twitter (X) data, which may not fully capture the views and sentiments of
the broader American and Canadian public. Fourth, our analysis is restricted
to tweets in English. In the context of Canada, this means we are capturing
only or primarily the perspectives of either anglophones or bilingual
francophones, which could potentially bias our data; for example, the high
levels of polarization observed in Quebec on COVID-19 measures may be
influenced by this language bias. Finally, while several of our analyses rely
on correlations, it is crucial to remember that these results do not imply
causation; the relationship between polarization and public health measures is
complex and multi-dimensional.

###  4.4 Conclusion

To conclude, our method has provided valuable insights into the dynamics of
partisan polarization during the COVID-19 pandemic. Political ideology, public
trust, and key events have emerged as important factors influencing public
discussions on pandemic-related issues in the United States and Canada. By
combining our polarization measure with other data, researchers and
practitioners can better understand how polarization varies across location,
time, and specific issues. This knowledge could help in detecting particularly
polarizing discussions on social media and in developing communication
strategies to mitigate the spread of misinformation, both for the current
pandemic and for future health-related crises.

The differences observed between these two countries are somewhat harder to
explain. Our analysis, along with insights from recent studies, suggests that
Canadian responses to public health measures could explain the lower levels of
polarization found in Canada. Indeed, there was a significant consensus on the
effectiveness of stay-at-home orders (i.e., lockdowns), mask mandates, and
vaccines not only at the federal and provincial levels, but also within the
news media. And unlike the U.S., where an important number of Republican
leaders aligned with Trump’s anti-mask and anti-lockdown positions, the
pandemic did not become a salient partisan issue within a political campaign
until much later in 2021. Prior to this, the opposition to public health
measures in Canada was primarily found in online communities, outside of the
mainstream media and political parties, where protesters remained heavily
influenced by American sources. Although our results suggest that social
networks contributed to the diffusion of these opinions during the COVID-19
pandemic, more work needs to be done to quantify the impact of online
communities interactions on polarization.

##  5 Methodology

In the following section, we describe in detail our text-based measurement of
partisan polarization. We first explain the data collection process. We then
show how we classified tweets into respective topics, geo-located users and
grouped them by party affiliation. Finally, we describe the equation used to
measure partisan polarization as well as our approximation algorithm. Figure 1
provides a visual overview of our process in measuring partisan polarization.
For additional details, please refer to Section 8 in the Supplementary
Material.

###  5.1 Data Collection

####  5.1.1 Twitter (X) Data

We used Twitter’s (X) official API to collect 1% of real-time tweets for
Canada and the United States from October 9, 2020 to January 4, 2021. This
represents 231,841,790 tweets and 4,765,115 users for Canada (a dataset
filtered for COVID and politics) and 387,090,097 tweets and 23,758,112 users
for the United States (a dataset filtered for election politics). We fed the
following list of keywords in the API to filter relevant tweets:

Canada: ‘trudeau’, ‘legault’, ‘doug ford’, ‘pallister’, ‘horgan’, ‘scott moe’,
‘jason kenney’, ‘dwight ball’, ‘blaine higgs’, ‘stephan mcneil’, ‘cdnpoli’,
‘canpol’, ‘cdnmedia’, ‘mcga’, ‘covidcanada’ and all combinations of ‘covid’ or
‘coronavirus’ as the prefix and the (full & abbreviated) name of each
provinces and territories as the suffix.

United States: ‘JoeBiden’, ‘DonaldTrump’, ‘Biden’, ‘Trump’, ‘vote’,
‘election’, ‘2020Elections’, ‘Elections2020’, ‘PresidentElectJoe’, ‘MAGA’,
‘BidenHaris2020’, ‘Election2020’.

###  5.2 COVID-19 Vaccination Rate

Similar to the COVID-19 pandemic data, we also used the officially reported
vaccination rate of the populations. We used the vaccination rates one year
later compared to the Twitter (X) data, as COVID-19 vaccines were created and
approved at the very end of our data collection process. Thus, the vaccination
rates are for those who obtained at least two doses. For Canada, this is the
$numtotal\\_fully$ from the government’s vaccine coverage map. We normalize
this column by Canada’s 2021 population per province or territory. For the
United States, we use the $people\\_fully\\_vaccinated\\_per\\_hundred$
reported in the COVID Data Tracker from the CDC.

###  5.3 Classifying Tweets By Topics

For this study, we looked into three key topics for COVID-19: lockdowns, masks
and vaccines. We also looked into conspiracy theories. For each topic, tweets
were classified as relevant or irrelevant to the topic based on whether they
contained at least one of the topic-specific keywords. For conspiracy-related
tweets, relevant means that the content is related to COVID-19 conspiracy
theories (either supporting or opposing). A tweet can belong to more than one
topic.

We first used a hashtag-based filtering step. We extracted all hashtags within
our dataset, ordered it by frequency, and discarded those that appeared less
than 100 times. This filtered list contained 3,600 hashtags for Canada and
18,000 for the United States. Two political scientists manually annotated this
list with topic and relevance labels. The list was narrowed to only those
hashtags labeled as relevant, resulting in 631 relevant hashtags. We then
merged these with hashtags identified in previous studies for the same
topics—i.e., from refs. [93, 94, 95].

For Canada, this process resulted in 46,636,206 tweets and 1,757,675 users
that shared content related to COVID-19. For the United States, this
represents 12,552,213 tweets and 2,657,355 users. Using the RoBERTa-base model
[31] from HuggingFace, we further pre-trained this model on the respective
COVID-19 tweets from each country dataset—i.e., performing a self-supervised
learning on predicting masked words within tweets. This results in two
different country-specific pre-trained language models for COVID-19 tweets.

We then randomly sampled 200 relevant and 200 irrelevant tweets per topic from
each dataset, for a total of 1,600 tweets. The same two political scientists
manually reviewed each tweet separately to determine if the tweet was
relevant/irrelevant. We discarded tweets where the annotators could not reach
a consensus. We then trained the respective pre-trained RoBERTa-base model on
each dataset to classify by topics—i.e., 4 topic language models per country,
for a total of 8 language models. We report the support, Cohen Kappa, F1-score
and number of tweets we extracted for each topic within each dataset in Table
4. Our analysis achieved a near perfect F1-score for each of these topics.

###  5.4 Classifying Users by Geo-Location

We wanted to quantify users in each province or state represented in our data,
as the users retrieved from Twitter (X) could be imbalanced relative to region
population size. For this, we geolocated all users with an explicit location
provided in the location field, a free-form text, as part of their profile
information. We process the information with Open Street Map and the ArcGIS
API. Both of these return a latitude and longitude if a location was found. We
set the threshold for a clear geolocation if both API responded with a
latitude and longitude that was within one degree of each other. We found this
to be more accurate, and preferable to using a pre-trained Named Entity
Recognition algorithm; most of the user-provided locations can be handled by
these Geographic Information System APIs, and the APIs could also provide
important details such as the country, state and city. We correlated the
geolocated users with the official population census for each country’s
region. In total, we geolocated 282,454 users with strong correlation of 0.92
(n=13, p=6.20e-06, CI=[0.76, 0.98]) for Canada and 757,601 users with strong
correlation of 0.98 (n=52, p=9.27e-35, CI=[0.96, 0.99]) for the United States.
This means that each region is well represented in our data. Further
information is reported in Table 5.

###  5.5 Classifying Users By Party Affiliation

We determine a user’s party affiliation using a two step approach. First, we
classify politically explicit users based on their profiles. We then use the
predictions from this profile classifier as labels to train a classifier based
on the user activity. We report the support, F1-score, and number of users we
classified for each party within each dataset in Table 6 for Canada and Table
9 for the United States. We achieve a macro-F1-score of 91% for both Canada
and the United States.

##### Profile Classifier

As a preprocessing step, we filter out users that are not politically
explicit. Politically explicit users are those whose profile description
contains at least one political keyword defined for any political party. For
Canada, we focused on the five main political parties: Conservative, Green,
Liberal, New Democratic Party and People’s Party. For the United States, we
focused on the Democratic and Republican parties. The following is the set of
keywords we have per party:

Canada:

Conservative \- ‘erin o’toole’, ‘andrew scheer’, ‘conservative’, ‘conservative
party’, ‘cpc’, ‘cpc2021’, ‘cpc2019’, ‘conservative party of canada’

Green \- ‘annamie paul’, ‘green party’, ‘gpc’, ‘gpc2019’, ‘gpc2021’, ‘green
party of canada’

Liberal \- ‘justin trudeau’, ‘liberal’, ‘liberal party’, ‘lpc’, ‘lpc2021’,
‘lpc2021’, ‘lpc2019’, ‘liberal party of canada’

New Democratic Party \- ‘jagmeeet singh’, ‘new democrat’, ‘new democrats’,
‘new democratic party’, ‘ndp’, ‘ndp2021’, ‘ndp2019’

People’s Party \- ‘maxime bernier’, ‘people’s party’, ‘ppc’, ‘ppc2019’,
‘ppc2021’, ‘people’s party of canada’

United States:

Democrat \- ‘liberal,’ ‘progressive,’ ‘democrat,’ ‘biden’

Republican \- ‘conservative,’ ‘gop,’ ‘republican,’ ‘trump,’

We then randomly selected a set of politically explicit users for each party
to be manually annotated by two political scientists. We only use party labels
that both annotators agreed upon. The Cohen Kappa score for the pair of
annotation sets is 0.74 and 0.76 for Canada and the United States
respectively. We then train a RoBERTA-large model with a 80-20 train-test
split to determine user party affiliation (see the profile classifier section
for more details). Exact numbers can be found in their respective tables in
the supplementary.

##### Activity Classifier

For this second phase, we make use of the respective RoBERTa-base model pre-
trained on COVID-19 tweets for each dataset to extract the tweet embeddings
(768-dimensional vector). We then generate user embeddings (768-dimensional
vector) by pooling together (mean aggregation) all tweet embeddings from that
user.

We then train an MLP consisting of two fully connected layers with the user
embeddings as input to predict the party affiliation. Before training, we
filtered out users based on their activity $\alpha$ (i.e., number of
COVID-19-related tweets in the dataset). We performed a hyperparameter search
for $\alpha$ among $\\{1,3,5,10,15,20\\}$ using 5-fold cross validation. This
was 5 tweets for Canada and 10 tweets for the United States.

Specifically for Canada, we found that the MLP could not distinguish the
parties sufficiently. Hence, we grouped the parties based on their partisan
leaning. The liberal (left) party family included the Liberal Party, New
Democratic Party and Green Party while the conservative (right) party family
included the Conservative Party and People’s Party. We removed supporters of
other minor parties and the Bloc Quebecois.

##### External Evaluation

Following the best practice for evaluating party affiliation predictions [96],
we matched Twitter (X) users from the United States with the primary voter
registration records available for five states: Ohio, New York, Florida,
Arkansas, North Carolina, as well as Washington DC. We describe this procedure
and its result in more details in the supplementary material 10. We achieve an
accuracy of 74.35% for the profile classifier and 73.35% for the activity
classifier. Both classifiers are binary, but users can also be independent or
support a third party, despite an ideology (and behavior/voting) that aligns
strongly with one of the two main parties. They can also have an outdated
registration that no longer reflects the beliefs they currently hold and
express on Twitter (X). Therefore, although accuracy here is lower than in our
training model, it still indicates our classification is acceptable and on par
with the standard for this type of evaluation [96].

###  5.6 Measuring Partisan Polarization

Given a set of political parties $\mathcal{P}$ and a set of given user
embeddings $\mathcal{U}=\\{{u^{(1)},u^{(2)},\dots,u^{(n)}}\\}$ where
$u^{(i)}\in\mathbb{R}^{768}$ and $n$ is the number of users, we measure
polarization as follows.

We first look at the distance and dispersion between each party,
$p\in\mathcal{P}$. We base our measure on the C-index of Hubert [32] to
quantify the extent of clustering and overlap of each political party. This is
done by first calculating the sum of inter-cluster distances:

$S_{w}=\frac{1}{2}\sum_{p\in\mathcal{P}}\sum_{u,v\in p}||u-v||$ (1)

We then normalize this value based on its minimum and maximum possible ranges,
$S_{min}$ and $S_{max}$. These correspond to the sum of the $m$ smallest
(resp. largest) distances between points in $\mathcal{U}$; where
$m=\sum_{p\in\mathcal{P}}\frac{|p|(|p|-1)}{2}$.

Based on these, we define our polarization index $poli$ as:

$poli=\frac{S_{max}-S_{w}}{S_{max}-S_{min}}$ (2)

The minimum value 0 represents no polarization, whereas the maximum value 1
represents the most extreme possible polarization, i.e., $p\in\mathcal{P}$ are
completely isolated from each other.

###  5.7 Approximation of Polarization

Equation 2 is not scalable to large $n$, as it is $O(n^{2}\log(n^{2}))$. We
approximate it by sub-sampling a sufficient set of users which enables us to
scale to a large number of users. To determine the minimum sample size needed,
we use the coefficient of variation, which is defined as $\frac{std}{mean}$
and expressed as a percentage. Generally, a coefficient of variation under 10
gives reasonable results [97].

Algorithm 1 summarizes this procedure. This approximation allows us to scale
our measure significantly without compromising accuracy. One loop of the
approximation has a time complexity of $O(rf^{2}*n^{2}\log((n)^{2}))$ where
$r$ is the repeat count and $f$ is the fraction (e.g., 0.01). From our
testing, we know that at large values of $n$, the fraction needed rarely
increases, so only one loop is required. Therefore, the time saved is $rf^{2}$
for large values of $n$.

We test the accuracy of approximation in Algorithm 1 over the daily lockdown
and vaccine tweets. In Figure 19(a), we plot the total number of users against
the absolute error (and its standard deviation) of the approximated $poli$
compared to the exact value, binned for every 1,000 users. We observe a
dramatic drop of the absolute error term at around 3,000 users. When we reach
the 10,000 users value, the absolute error is usually below 0.001. In Figure
19(b), we plot the total number of users against the time saved in running the
approximation algorithm compared to running the exact $poli$, binned also for
every 1,000 users. We observe that the time saved is exponential to the number
of users. We note that at around 50,000 users, the approximation rarely needs
to increase the fraction of users sampled.

These findings confirm that we can accurately approximate $poli$ for large-
scale data that is impossible to measure exactly because of memory
constraints. As $poli$ relies heavily on finding pairwise distances (time and
memory intensive), we see from our analysis that a sampling approach can save
both time and memory exponentially.

Algorithm 1 Approximating $poli$

1:$\mathcal{U}$, $fraction$, $epsilon$, $step\\_size$, $repeats$

2:while $cv\\_poli>epsilon$ do

3: $poli\\_indices\leftarrow[]$

4: $num\\_of\\_runs\leftarrow 0$

5: for $i$ in $repeats$ do

6: $\mathcal{U}_{s}\leftarrow sample(\mathcal{U},fraction)$

7: $poli\\_indices.append(poli(\mathcal{U}_{s}))$

8: end for

9: $mean\\_poli\leftarrow mean(poli\\_indices)$

10: $std\\_poli\leftarrow std(poli\\_indices)$

11: $cv\\_poli\leftarrow std\\_poli/mean\\_poli$

12: $fraction\leftarrow fraction+step\\_size$

13:end while

14:return $mean\\_poli$

##  6 Acknowledgements

This research is supported by CIFAR AI Catalyst Grants and Canada CIFAR AI
Research Chair funding.

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##  7 Extended Results

###  7.1 Regional Variations of Partisan Polarization

Figure 10: Ranking of American states by partisan polarization per topic.
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
party ratio from the 2020 United States Presidential Election, where more blue
means more users voted for the Democratic Party and more red means more users
voted the Republican Party. We can see that red states are mostly ranked
higher than blue states. Figure 11: Partisan polarization ranking of Canadian
provinces and territories per topic. 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 based on the party ratio from
Canada’s 2021 Federal Election, where more blue means more users from the
liberal (left) party family (Liberal, New Democratic Party, Green), and more
red means more users from the conservative (right) party family (Conservative,
People’s Party).

In fig. 10 and fig. 11, we show the ranking of the regions (American states
and Canadian provinces and territories, respectively) by partisan polarization
per topic.

Figure 12: Correlation between Polarization Score and Vote Margin for
Conservative Party. Colors are vote margin. Respective Pearson $r$ correlation
and corresponding $p$-value is shown in each panel. Significant correlation
between Polarization Score and Vote Margin is found for the US discourse on
Masks and on Vaccines.

In fig. 12, we show the correlation in the polarization scores and relative
vote margins. The latter shows that states in which the margin by which
Republican party votes exceeded those of the Democractic party correlates
significantly with the amount of polarization exhibited by the Twitter (X)
discourse in those states, when conditioning the discourse on masks and on
vaccines, but though significantly when conditioning on lockdowns.

###  7.2 The Relationship between Regional Vaccine Polarization and
Vaccination Rates in Canada

In Figure 13, we remove Nunavut as an outlier because of its very small
population. While we get strong positive correlation with vaccination rate, it
is over a relatively low number of points. In Canada, vaccines were mandated,
requiring vaccine passports to be served in public areas. We assume that
vaccine partisan polarization increases, as people are not happy with being
forced to be vaccinated, but most of the population still are vaccinated.
However, with the few points, we do not have a definite conclusion for this
result.

Figure 13: Relation between vaccine polarization and vaccination rates in
Canada. The correlation is 0.74 with CI = [0.31, 0.92] (n = 12, p = 0.004).
The Vaccine Partisan Polarization for each province or territory is computed
weekly and averaged over 12 weeks from October 11, 2020 to January 3, 2021.
Official vaccination rates for different regions are obtained from Statistic
Canada. The Vaccination Rate is also averaged weekly for the similar period of
time a year into future to be after the vaccines were rolled out, i.e. October
11, 2021 to January 3, 2022. Color for the scatter plots is determined by the
respective party ratio from the 2021 Canadian federal election.

###  7.3 Specific Regional Partisan Polarization and COVID-19 Deaths

Here, we investigate the topic-specific polarization over time and how it
relates to the reported number of Deaths for COVID-19 for specific regions in
Figure 14 for Canada and in Figure 15 for the United States.

(a) Weekly Partisan Polarization & Death Rate in Alberta

(b) Weekly Partisan Polarization & Death Rate in British Columbia

(c) Weekly Partisan Polarization & Death Rate in Ontario

(d) Weekly Partisan Polarization & Death Rate in Quebec

Figure 14: Weekly trends of partisan polarization in Canada for the top 4
largest provinces from October 11, 2020 to January 3, 2021. We report the
average death rate (red dotted line) per week and report the correlation
between the topic-specific correlation with the death rate in the brackets in
the legend.

(a) Weekly Partisan Polarization & Death Rate in Mississippi

(b) Weekly Partisan Polarization & Death Rate in Vermont

(c) Weekly Partisan Polarization & Death Rate in Delaware

(d) Weekly Partisan Polarization & Death Rate in Iowa

Figure 15: Weekly trends of partisan polarization in the United States for
highest ranked state overall (a), lowest ranked state (b), highest ranked
liberal state (c) and lowest ranked conservative state (d) from October 11,
2020 to January 3, 2021. We report the average death rate (red dotted line)
per week and report the correlation between the topic-specific correlation
with the death rate in the brackets in the legend.

###  7.4 Correlation Matrices

Table 2: Correlation Matrix between Topic Polarization and External Data in Canada. Bolded means $p<0.001$. Italicized means $p<0.01$. Underline means $p<0.05$. Background color of green or red signifies the positive or negative correlation for significant p-values only. |  | Cases | Deaths | Conspiracy (Volume) | Stringency Index
---|---|---|---|---|---
| Polarization |  | -0.338
---
CI=[-0.511,-0.138]
p=0.001
| -0.280
---
CI=[-0.462,-0.075]
p=0.008
| 0.190
---
CI=[-0.020,0.384]
p=0.076
| -0.518
---
CI=[-0.657,-0.347]
p=0.000
| Volume |  | 0.244
---
CI=[0.037,0.432]
p=0.022
| 0.171
---
CI=[-0.040,0.367]
p=0.112
| -0.084
---
CI=[-0.288,0.128]
p=0.437
| 0.259
---
CI=[0.052,0.444]
p=0.015
| % Volume |  | -0.368
---
CI=[-0.536,-0.172]
p=0.000
| -0.340
---
CI=[-0.513,-0.141]
p=0.001
| 0.149
---
CI=[-0.062,0.348]
p=0.165
| -0.391
---
CI=[-0.555,-0.198]
p=0.000
Lockdown |  | Weighted
---
Polarization
| -0.402
---
CI=[-0.564,-0.210]
p=0.000
| -0.368
---
CI=[-0.536,-0.172]
p=0.000
| 0.172
---
CI=[-0.039,0.368]
p=0.110
| -0.443
---
CI=[-0.597,-0.258]
p=0.000
| Polarization |  | 0.117
---
CI=[-0.095,0.318]
p=0.279
| 0.102
---
CI=[-0.110,0.305]
p=0.345
| -0.132
---
CI=[-0.332,0.080]
p=0.221
| 0.011
---
CI=[-0.198,0.220]
p=0.916
| Volume |  | -0.202
---
CI=[-0.395,0.008]
p=0.059
| -0.306
---
CI=[-0.485,-0.104]
p=0.004
| 0.462
---
CI=[0.280,0.612]
p=0.000
| -0.267
---
CI=[-0.452,-0.061]
p=0.012
| % Volume |  | -0.688
---
CI=[-0.784,-0.559]
p=0.000
| -0.689
---
CI=[-0.785,-0.561]
p=0.000
| 0.615
---
CI=[0.465,0.730]
p=0.000
| -0.804
---
CI=[-0.867,-0.715]
p=0.000
Mask |  | Weighted
---
Polarization
| -0.687
---
CI=[-0.783,-0.557]
p=0.000
| -0.688
---
CI=[-0.784,-0.559]
p=0.000
| 0.610
---
CI=[0.459,0.726]
p=0.000
| -0.804
---
CI=[-0.867,-0.715]
p=0.000
| Polarization |  | 0.271
---
CI=[0.066,0.455]
p=0.011
| 0.361
---
CI=[0.164,0.530]
p=0.001
| -0.202
---
CI=[-0.395,0.007]
p=0.059
| 0.254
---
CI=[0.047,0.440]
p=0.017
| Volume |  | 0.718
---
CI=[0.599,0.806]
p=0.000
| 0.658
---
CI=[0.520,0.762]
p=0.000
| -0.364
---
CI=[-0.533,-0.168]
p=0.000
| 0.711
---
CI=[0.590,0.801]
p=0.000
| % Volume |  | 0.683
---
CI=[0.553,0.781]
p=0.000
| 0.672
---
CI=[0.538,0.773]
p=0.000
| -0.532
---
CI=[-0.667,-0.363]
p=0.000
| 0.781
---
CI=[0.684,0.851]
p=0.000
Vaccine |  | Weighted
---
Polarization
| 0.687
---
CI=[0.558,0.784]
p=0.000
| 0.678
---
CI=[0.546,0.777]
p=0.000
| -0.533
---
CI=[-0.668,-0.364]
p=0.000
| 0.782
---
CI=[0.684,0.852]
p=0.000
| Sum |  | -0.121
---
CI=[-0.322,0.091]
p=0.261
| -0.044
---
CI=[-0.251,0.167]
p=0.681
| 0.023
---
CI=[-0.187,0.231]
p=0.831
| -0.315
---
CI=[-0.492,-0.113]
p=0.003
Aggregated |  | Weighted
---
Sum
| -0.062
---
CI=[-0.268,0.149]
p=0.566
| 0.027
---
CI=[-0.183,0.236]
p=0.799
| -0.032
---
CI=[-0.240,0.178]
p=0.764
| -0.256
---
CI=[-0.442,-0.049]
p=0.016
Table 3: Correlation Matrix between Topic Polarization and External Data in the United States. Bolded means $p<0.001$. Italacized means $p<0.01$. Underline means $p<0.05$. Background color of green or red signifies the positive or negative correlation for significant p-values only. |  | Cases | Deaths | Conspiracy (Volume) | Stringency Index
---|---|---|---|---|---
| Polarization |  | -0.020
---
CI=[-0.244,0.207]
p=0.867
| -0.070
---
CI=[-0.291,0.158]
p=0.548
| -0.146
---
CI=[-0.360,0.082]
p=0.207
| 0.010
---
CI=[-0.216,0.235]
p=0.931
| Volume |  | -0.196
---
CI=[-0.403,0.031]
p=0.090
| -0.319
---
CI=[-0.508,-0.100]
p=0.005
| 0.497
---
CI=[0.306,0.650]
p=0.000
| -0.262
---
CI=[-0.460,-0.038]
p=0.022
| % Volume |  | -0.394
---
CI=[-0.569,-0.185]
p=0.000
| -0.436
---
CI=[-0.602,-0.233]
p=0.000
| 0.120
---
CI=[-0.109,0.336]
p=0.303
| -0.356
---
CI=[-0.538,-0.142]
p=0.002
Lockdown |  | Weighted
---
Polarization
| -0.395
---
CI=[-0.570,-0.186]
p=0.000
| -0.442
---
CI=[-0.607,-0.240]
p=0.000
| 0.101
---
CI=[-0.128,0.319]
p=0.387
| -0.354
---
CI=[-0.537,-0.140]
p=0.002
| Polarization |  | -0.103
---
CI=[-0.321,0.125]
p=0.376
| -0.108
---
CI=[-0.325,0.121]
p=0.355
| -0.078
---
CI=[-0.298,0.150]
p=0.502
| -0.121
---
CI=[-0.337,0.107]
p=0.298
| Volume |  | -0.311
---
CI=[-0.501,-0.092]
p=0.006
| -0.378
---
CI=[-0.556,-0.167]
p=0.001
| 0.500
---
CI=[0.310,0.652]
p=0.000
| -0.288
---
CI=[-0.482,-0.067]
p=0.012
| % Volume |  | -0.541
---
CI=[-0.683,-0.360]
p=0.000
| -0.526
---
CI=[-0.672,-0.341]
p=0.000
| -0.012
---
CI=[-0.237,0.214]
p=0.915
| -0.452
---
CI=[-0.615,-0.252]
p=0.000
Mask |  | Weighted
---
Polarization
| -0.565
---
CI=[-0.701,-0.390]
p=0.000
| -0.547
---
CI=[-0.688,-0.367]
p=0.000
| -0.020
---
CI=[-0.245,0.206]
p=0.861
| -0.474
---
CI=[-0.632,-0.279]
p=0.000
| Polarization |  | -0.246
---
CI=[-0.446,-0.021]
p=0.033
| -0.216
---
CI=[-0.421,0.010]
p=0.061
| -0.268
---
CI=[-0.466,-0.046]
p=0.019
| -0.133
---
CI=[-0.348,0.096]
p=0.253
| Volume |  | 0.360
---
CI=[0.147,0.542]
p=0.001
| 0.305
---
CI=[0.085,0.496]
p=0.007
| 0.303
---
CI=[0.084,0.495]
p=0.008
| 0.196
---
CI=[-0.031,0.404]
p=0.089
| % Volume |  | 0.666
---
CI=[0.518,0.775]
p=0.000
| 0.682
---
CI=[0.539,0.786]
p=0.000
| -0.068
---
CI=[-0.289,0.160]
p=0.561
| 0.573
---
CI=[0.400,0.707]
p=0.000
Vaccine |  | Weighted
---
Polarization
| 0.633
---
CI=[0.475,0.751]
p=0.000
| 0.655
---
CI=[0.505,0.767]
p=0.000
| -0.115
---
CI=[-0.332,0.113]
p=0.322
| 0.576
---
CI=[0.403,0.710]
p=0.000
| Sum |  | -0.184
---
CI=[-0.393,0.044]
p=0.112
| -0.196
---
CI=[-0.403,0.031]
p=0.090
| -0.247
---
CI=[-0.448,-0.023]
p=0.031
| -0.119
---
CI=[-0.335,0.110]
p=0.307
Aggregated |  | Weighted
---
Sum
| -0.217
---
CI=[-0.422,0.009]
p=0.060
| -0.198
---
CI=[-0.405,0.029]
p=0.086
| -0.193
---
CI=[-0.401,0.034]
p=0.095
| -0.091
---
CI=[-0.310,0.137]
p=0.434

###  7.5 The Relationship between National Partisan Polarization and
Epidemiological Data

Here, we investigate the aggregated polarization over time for each country
and how it relates to the reported number of New Cases and Deaths for
COVID-19. In Figure 16, we observe that polarization is not correlated with
the severity of the pandemic, in both Canada and the United States. To compute
the daily aggregate polarization measure, we employ a weighted sum of each
topic’s polarization, considering the percentage of each topic’s tweets within
that day’s volume of COVID-19-related tweets.

(a) Canada: Daily Polarization Trends

(b) Canada: Polarization v.s. Deaths

(c) United States: Daily Polarization Trends

(d) United States: Polarization v.s. Deaths

Figure 16: Daily aggregate polarization v.s. COVID-19 new cases and deaths for
(a) Canada and (c) the United States from October 9, 2020 to January 3, 2021.
Correlation of polarization and COVID-19 deaths for (b) Canada and (d) the
United States. The correlation coefficient are -0.044 for Canada with CI =
[0.251,0.167] (n = 88, p = 0.681) and -0.196 for the United States with CI =
[-0.403,0.031] (n = 88, p = 0.090).

##  8 Methodology Details

In the following section, we report the classification metrics of each module
in the pipeline for measuring polarization.

###  8.1 Classifying Tweets by Topics

Table 4: Tweet Topic Classification Metrics. 200 relevant and 200 irrelevant
tweets were sampled for manual annotation. F1-score is calculated on the true
labels where both annotators agreed upon over 5 runs with a different random
seed. Canada
---
Topic | Relevant | Irrelevant | Cohen | F1-Score | # of Tweets
Lockdown | 170 | 356 | 0.73 | 97.13 ± 1.57 | 1,553,984
Mask | 292 | 282 | 0.91 | 98.48 ± 1.24 | 1,994,293
Vaccine | 326 | 248 | 0.91 | 99.84 ± 0.31 | 2,145,549
Conspiracy | 338 | 171 | 0.67 | 97.20 ± 0.71 | 16,575,934
United States
Topic | Relevant | Irrelevant | Cohen | F1-Score | # of Tweets
Lockdown | 126 | 199 | 0.63 | 100.00 ± 0.00 | 897,565
Mask | 197 | 200 | 0.98 | 99.49 ± 0.62 | 1,562,706
Vaccine | 192 | 201 | 0.96 | 100.00 ± 0.00 | 1,541,360
Conspiracy | 195 | 155 | 0.75 | 95.55 ± 2.39 | 926,389

###  8.2 Classifying Users by Geolocation

Table 5: Geolocated Users Number and Correlation. Correlation is done with the
official 2021 population census for each country. Canada
---
Users | Total | Correlation
Geolocated | 282,454 |  0.92 (n=13, p=6.20e-06, CI=[0.76, 0.98])
w/ Party Affiliation | 195,456 |  0.92 (n=13, p=6.83e-06, CI=[0.76, 0.98])
United States
Users | Total | Correlation
Geolocated | 757,601 |  0.98 (n=52, p=9.27e-35, CI=[0.96, 0.99])
w/ Party Affiliation | 242,056 |  0.97 (n=52, p=2.51e-34, CI=[0.96, 0.99])

###  8.3 Classifying User Party Affiliation

We report the classification metrics for Canada in Table 6 and for the United
States in Table 9. For Canada, our model classified users for each parties for
the activity as well, but the F1-score was not satisfactory as parties within
the liberal (left) party family and conservative (right) party family was
easily confused as shown in the confusion matrix in Table 7. Thus, for our
analysis, we merged the parties in Canada, and show the confusion matrix for
after the merge in Table 8.

Table 6: Canadian User Party Affiliation Classification. Cohen Kappa score of 0.74 | Party | Support | F1-Score | # of Users
---|---|---|---|---
Profile | CPC | 98 | 92.93 ± 1.12 | 1,769
GPC | 60 | 88.50 ± 1.54 | 97
LPC | 100 | 90.89 ± 1.34 | 783
NDP | 124 | 93.44 ± 0.53 | 370
NO_PARTY | 105 | 86.16 ± 1.97 | 667
PPC | 95 | 94.09 ± 1.34 | 402
Activity | RPF | 628 | 93.85 ± 0.47 | 193,225
LPF | 357 | 89.10 ± 0.73 | 299,836
Combined | RPF | – | – | 196,338
LPF | – | – | 302,023
Table 7: Canadian User Party Affiliation Activity Classifier Confusion Matrix | CPC | PPC | GPC | LPC | NDP
---|---|---|---|---|---
CPC | 398 | 24 | 1 | 29 | 6
PPC | 59 | 49 | 0 | 1 | 1
GPC | 3 | 0 | 6 | 10 | 6
LPC | 18 | 3 | 2 | 161 | 26
NDP | 5 | 1 | 1 | 19 | 58
Table 8: Canadian User Party Affiliation Activity Classifier Confusion Matrix - Liberal (left) and Conservative (right) Party Family | Right Party Family | Left Party Family
---|---|---
Right Party Family | 530 | 38
Left Party Family | 27 | 289
Table 9: American User Party Affiliation Classification. Cohen Kappa score of 0.76. Total 763,164 users. | Party | Support | F1-Score | # of Users
---|---|---|---|---
Profile | Republican | 854 | 97.21 ± 0.66 | 86,989
Democrat | 928 | 97.40 ± 0.63 | 82,923
Activity | Republican | 10,583 | 92.98 ± 0.18 | 239,449
Democrat | 10,226 | 92.99 ± 0.16 | 145,733
Combined | Republican | – | – | 336,231
Democrat | – | – | 426,933

###  8.4 Distribution Matching with Election Results

We further validate our party affiliation distribution using the 2019 Canada
Federal Election and the US 2020 Election results for the respective country.
We calculate the correlation between the ratio of numbers of liberal and
conservative families-labelled users per region in our data compared to the
election results and obtain strong correlations of 0.815 for Canada visualized
in Figure 17 and 0.802 for the United States visualized in Figure 18. We also
visualize the ratio for all geolocated users for the respective topics.

(a) 2019 Election Results

(b) Geolocated Users

(c) Lockdown

(d) Mask

(e) Vaccine

(f) Conspiracy

Figure 17: Normalized distribution of the inferred user party affiliations
compared to the CAD 2021 election results.

(a) 2020 Election Results

(b) Geolocated Users

(c) Lockdown

(d) Mask

(e) Vaccine

(f) Conspiracy

Figure 18: 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.

###  8.5 Matching Users to US Voter Registration

Following the best practice for evaluating party affiliation predictions [96],
we matched Twitter (X) users from our dataset with the primary voter
registration records available for five states: Ohio, New York, Florida,
Arkansas, North Carolina, as well as Washington DC. From these records, we
obtain the party affiliation of unique users in each state by md5-hashing
their names and county to construct a key identifier. From our set of
geolocated Twitter (X) users, we kept everyone that belonged to one of the
five states or DC, and we removed those whose location could not be retrieved.
Finally, we matched the most recent voter party affiliation records from the
registration data to the unique Twitter (X) users that matched both the county
and either the first name and last name or the first, middle and last name. We
pre-processed the user’s name on Twitter (X) to remove emojis. After matching,
we removed users not affiliated with either of the two major parties and users
whose name matched with more than one record per county (indicating a non-
unique match).

We then compare users’ party from voter registration with their predicted
party, first using their profile description and second their COVID-19-related
tweets. Using our geolocation and voter record matching, Table 10 shows we are
able match a significant number of users, more than 30k, across the 5 states
and DC with their voter records.

Table 10: Users matched to their voter registration. Voters* and Users* respectively correspond to the number of unique voters in the records and unique Twitter (X) users in our data. State | Voters* | Users* | Matched | Democrat | Republican | Other
---|---|---|---|---|---|---
Ohio | 7,771,590 | 4,913 | 1,431 | 320 | 193 | 917
New York | 17,718,437 | 30,927 | 8,255 | 4,843 | 1,631 | 1,781
Florida | 14,477,882 | 50,541 | 12,905 | 5,585 | 4,508 | 2,810
Arkansas | 1,722,465 | 4,311 | 1,280 | 145 | 140 | 995
District of Columbia | 510,026 | 17,661 | 2,538 | 1,929 | 153 | 456
North Carolina | 8,004,814 | 20,761 | 6,050 | 2,450 | 1,655 | 1,945
Total |  |  | 32,456 | 15,272 | 8,280 | 8,904

We get an accuracy of 74.35% for the first method and 73.35% for the second
one. We note that both methods are binary classifiers while users can also be
independent or support a third party, despite an ideology (and
behavior/voting) that aligns strongly with one of the two main parties. They
can also have an outdated registration that no longer reflects the beliefs
they currently hold and express on Twitter (X). Therefore, although accuracy
here is lower, it still indicates our classification is acceptable and on par
with the standard for this type of evaluation [96].

###  8.6 Approximation of Polarization

We test accuracy of approximation in Algorithm 1 over the daily Lockdown and
Vaccine tweets.

In Figure 19(a), we plot the total number of users against the absolute error
(and its standard deviation) of the approximated $poli$ compared to the exact
value, binned for every 1,000 users. We observe a dramatic drop of the
absolute error term at around 3,000 users. When we reach the 10,000 users
value, the absolute error is usually below 0.001.

In Figure 19(b), we plot the total number of users against the time saved in
running the approximation algorithm compared to running the exact $poli$,
binned also for every 1,000 users. We observe that the time saved is
exponential to the number of users. We note that at around 50,000 users, the
approximation rarely needs to increase the fraction of users sampled.

These findings confirm that we can accurately approximate $poli$ for large-
scale data that is impossible to measure exactly because of memory
constraints. As $poli$ relies heavily on finding pairwise distances (time and
memory intensive), we see from our analysis how sampling can save both time
and memory exponentially.

(a) Approximation Quality

(b) Time Saved in Seconds

Figure 19:  For large enough data, the approximation error is close to 0.001.
Our approximation of polarization also exponentially saves time as the number
of instances grows.

We also explore the impact of changing the minimum coefficient of variation.
We start with a minimum sample size of 1% or $fraction=0.01$. We keep the
step_size constant at 0.01. For the first experiment, $repeats$ is set to
$10$. For the second experiment, shown here in figures 20 and 21, $epsilon$ is
set to $0.05$.

Figure 20: Approximation of $poli$. 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.

Figure 21: Approximation of $poli$. 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.