# Analysis of the influence of political polarization in the vaccination
stance:
the Brazilian COVID-19 scenario

Régis Ebeling, Carlos Abel Córdova Sáenz, Jeferson Nobre, Karin Becker
Institute of Informatics \- Federal University of Rio Grande do Sul (UFRGS)
Porto Alegre, Brazil
{rebeling, cacsaenz, jcnobre<EMAIL_ADDRESS>
###### Abstract

The outbreak of COVID‐19 had a huge global impact, and non-scientific beliefs
and political polarization have significantly influenced the population’s
behavior. In this context, COVID vaccines were made available in an
unprecedented time, but a high level of hesitance has been observed that can
undermine community immunization. Traditionally, anti-vaccination attitudes
are more related to conspiratorial thinking rather than political bias. In
Brazil, a country with an exemplar tradition in large-scale vaccination
programs, all COVID-related topics have also been discussed under a strong
political bias. In this paper, we use a multi-dimensional analysis framework
to understand if anti/pro vaccination stances expressed by Brazilians in
social media are influenced by political polarization. The analysis framework
incorporates: techniques to automatically infer from users their political
orientation, topic modeling to discover their concerns, network analysis to
characterize their social behavior, and the characterization of information
sources and external influence. Our main findings confirm that anti/pro-
stances are biased by political polarization, right and left, respectively.
While a significant proportion of pro-vaxxers display haste for an
immunization program and criticize the government’s actions, the anti-vaxxers
distrust a vaccine developed in a record time. Anti-vaccination stance is also
related to prejudice against China (anti-sinovaxxers), revealing
conspiratorial theories related to communism. All groups display an “echo
chamber” behavior, revealing they are not open to distinct views.

## Introduction

Since the outbreak in China in late 2019, the novel coronavirus disease
(COVID‐19) has quickly spread around the world. Due to the pandemic’s urgency,
efforts were undertaken to develop COVID-19 vaccines, approve and make them
available in the shortest possible time frame. These efforts had to follow the
same legal requirements for pharmaceutical quality, safety, and efficacy as
other medicines. COVID-19 immunization programs have started in Europe by the
end of 2020.

Despite being recognized as one of the most successful public health measures,
a growing number of people perceive the vaccination as unsafe and unnecessary
(?). Anti-vaccination movements have been implicated in lowered vaccine
acceptance rates and the increase in vaccine-preventable disease outbreaks.
Studies indicate that anti-vaccination attitudes are more related to
conspiratorial thinking rather than political bias or religious beliefs (?; ?;
?). In this context, SARS-CoV-2 vaccines have been the target of all sorts of
fake news and misinformation (?). Vaccine hesitancy and misinformation present
substantial obstacles to achieving coverage and community immunization (?).
Brazil has a successful history of immunization programs, and its National
Immunization Program (NIP) is a world-class reference on the large-scale
eradication of many diseases (?).

Political polarization has influenced the population’s behavior towards
COVID-19, including vaccination. In the United States of America (US), studies
(?; ?) reveal that partisan affiliation is often the strongest single
predictor of behavior towards COVID-19. Such political polarization was also
observed in Brazil, a country politically divided since the 2018 Presidential
election, when the right-wing candidate Jair Bolsonaro was the winner.
Political polarization has been a major obstacle in the planning and
implantation of a national COVID-19 immunization program, despite the large
experience with the NPI and the existence of national research centers able to
produce vaccines (e.g., Fiocruz, Butantan). In addition, substantial efforts
were not undertaken throughout 2020 to secure contracts with the international
pharmaceutical industries to buy vaccines.

At the same time, Brazilian governors and mayors, who have to deal with day-
by-day COVID-19 demands directly, are concerned about the collapse of the
health system and exert pressure for large-scale vaccination. Mandatory
vaccination is defended by many of them to reach community immunization, so as
to preserve lives and recover the economy. Joao Doria, governor of São Paulo,
has devised a state immunization program, securing funds to develop the
vaccine Coronavac in a joint effort between Instituto Butantan and the Chinese
pharmaceutical company Sinovac. Since Doria is a prospective candidate for the
2022 Presidential election, Bolsonaro has repeatedly undermined Doria’s
efforts regarding Coronavac, using derogatory and xenophobic terms to refer to
it, as well as making direct offenses to China (which resulted in the delay of
Chinese pharmaceutical inputs to produce Coronavac). The “Phony War” between
Bolsonaro and Doria has been fought mostly on social media. The Brazilian
vaccination program has started in late January 2021 with Coronavac.

Significant research efforts have addressed COVID-related discourse in social
media. Regarding the influence of political polarization, (?) examines
geographic differences in online discourses, (?) analyzes narratives according
to governmental decision making, (?) investigates its relationship with anti-
science behavior, and (?) characterizes the influence of political
polarization in groups pro/against social distance. Misinformation about COVID
is addressed in works such as (?; ?; ?), and the ideological influence is
stressed in (?). Regarding COVID-19 vaccination on social media, a few pre-
print works propose topic modeling techniques to provide insights on anti-
vaccination views (?; ?). To the best of our knowledge, existing works were
not successful in relating anti-vaccination behavior to political ideology
(e.g., (?; ?)).

In this paper, we use a multi-dimensional analysis framework to understand
Twitter users’ behavior with distinct stances on COVID-19 vaccination and
their relationship with the political polarization in the Brazilian scenario.
The analysis framework incorporates: a) topic modeling to discover the
specific concerns expressed by each group and their possible political bias;
b) an index to infer the political orientation of users based on the
politicians followed, c) network analysis and community detection to
characterize the effect of political polarization in the behavior as a social
network group; and d) the characterization of information sources and external
influence to evaluate the “echo chamber effect”. We analyze and compare a pro-
vaccination group, two anti-vaccination groups, and a neutral group. We
divided into two anti-vaccination groups because we identified a significant
number of users expressing derogatory ideas specifically related to Coronavac.
In this work, these groups are referred to as pro-vaxxers, anti-vaxxers and
anti-sinovaxxers. The latter is a subgroup of anti-vaxxers, in which the anti-
stance is not limited to the vaccination itself: it also encompasses the
partnership with the Chinese Sinovac and Bolsonaro’s political rival Joao
Doria.

In regards to the Brazilian scenario of COVID-19 vaccination, we aim to answer
the following research questions:

  * -

Q1: Is there a difference in the topics discussed by each group?

  * -

Q2: Are these groups politically polarized?

  * -

Q3: Does the polarization affect the social network structure of these groups?

  * -

Q4: Is there a difference in the information sources used by each group?

Our main findings confirm that anti/pro stances in the Brazilian COVID
vaccination scenario are biased by political polarization, right and left,
respectively. A significant proportion of pro-vaxxers display haste for an
immunization program and criticize the President and the government’s actions.
The anti-vaxxers distrust a vaccine developed in a record time and defend
immunization as an individual choice. Anti-vaccination stance is also related
to prejudice against China (anti-sinovaxxers), revealing conspiracy theories
related to communism and support to the rivalry between prospective candidates
for the 2022 Presidential election. As a group, the social networks of anti-
sinovaxxers and anti-vaxxers are more connected and act as spokespersons of
right-wing politicians, while mainstream media and science activists influence
the pro-vaxxers. All groups display an “echo chamber” behavior, revealing they
are not open to distinct views but rather seek to reinforce their convictions.

The remainder of this paper is structured as follows. Section 2Related Work
discusses related work. Section 3Analysis Framework discusses the data used
and the techniques deployed for the proposed analysis. Section 4Results
presents the analyses developed to answer the research questions. Section
5Threats to Validity discusses the validity threats to our study. Section
6Conclusions and Future Work draws conclusions and future work.

## Related Work

Twitter has been used to study different social phenomena, such as gender
equality (?), racial equity (?) or political polarization (?).

Different works have extracted information from user’s profiles, their social
structure, or information spread in the network to make predictions of
political partisanship. Works such as (?; ?) derive a user’s political
polarization by analyzing the users they follow in the social network.
Political polarization can also be inferred by analyzing clusters of
retweets/mentions of tweets from users (?) or by models analyzing a set of
features extracted from the tweets (?).

COVID-19 in social media is a very active research area, and we can find more
than 150 pre-print works in arXiv. Many works investigate online conversations
in terms of topics, information diffusion, and topics change overtime (?; ?).
Regarding political polarization, (?) examines geographic differences in
online COVID-19 discourse, relating the polarization to each US state’s
political dominance. (?) presents a longitudinal study relating Twitter
narratives to Governors and Presidential actions. (?) examines the ideological
alignment of users along moderacy, political and science dimensions,
concluding that moderacy is the key influence on science’s behavior. (?)
relates misinformation topics about the COVID-19 with political ideology,
concluding that liberals are more prone to believe/spread misinformation. (?)
analyzed multiple dimensions of the political influence in pro/anti social
distance stances. They concluded that these groups display similar
psychological aspects, but liberals tend to form a more cohesive, socially
connected group that are not interested nor open to other views.

The far-from-universal willingness to accept a COVID-19 vaccine represents
substantial obstacles to achieving coverage and community immunity (?). The
prevalent reasons for anti-vaccination attitudes are conspiratorial thinking,
followed by a low tolerance for impingement on their freedoms (?). Although
studies show that political partisanship is the strongest single predictor of
behavior regarding COVID (?; ?), to the best of our knowledge, ideology has
not found to be influential on the anti-vaccination stance. Studies reveal
that while anti-vaxxers will not change their view under any argument, the
pro-vaccine groups have been reactive and reticent due to legitimate questions
(?). According to (?), vaccination skeptics and advocates reside in their own
distinct “echo chamber”, but their network information structure is distinct.
Insights in the doubts and misinformation spreading have been addressed by
works such as (?; ?; ?; ?; ?).

Our study differs from related work by examining the political influence on
pro/anti-vaccination stances in the Brazilian COVID scenario. We investigate
Twitter users according to an analysis framework that combines topic modeling
for summarizing concerns, political polarization measuring, analysis of their
social network structure, and information sources.

## Analysis Framework

This paper investigates the political polarization influence on the
vaccination stances expressed by Brazilians on Twitter. We identified distinct
groups with anti/pro stances and deployed a multi-dimensional framework that
encompasses identification of concerns, polarization level, social network
properties, and information sources. Figure 1 displays the techniques
associated with each dimension, detailed in the remainder of this section.

Figure 1: Analysis Framework Table 1: Hashtags and collection numbers per
group

Group | Nº Tweets | Nº Users
---|---|---
Pro-vaxxers | 160.867 | 100.847
Anti-vaxxers | 32.876 | 15.647
Anti-sinovaxxers | 17.810 | 7.067
Neutrals | 19.558 | 18.396

Figure 2: iGPS with Brazilian Presidents and Political Influencers

### Data Crawling and Groups

To compose the groups with distinct vaccination stances, we crawled tweets
containing the terms “vaccine” or “vaccination” in Portuguese, from January
1st 2020 to April 1st 2021\. This period covers the pandemic since its
beginning, all phases of vaccine development and approval, as well as the
first trimester of vaccination in 2021. We collected a total of 236.992
tweets. Then, we extracted all hashtags, ordered by frequency, and grouped
them according to the represented stance. We identified four distinct groups
according to the meaning of the hashtags:

  * -

Pro-vaxxers: this group represents people who express a pro-vaccination
stance, by using hashtags that express endorsement for immunization programs
(e.g., VaccinesForEverybody), immunization intention (e.g.,
IWillReceiveVaccine) or immunization urgency (e.g., VaccineNow). The
portuguese hashtags are #EuVouTomarVacina, #VacinaBrasil,
#VacinaÉAmorAoPróximo, #VacinaJá, #VacinaNoBrasil, #VacinaParaTodos,
#VacinasPelaVida, #VacinaUrgenteParaTodos and #VemVacina;

  * -

Anti-vaxxers: this group represents people who took a stance against COVID-19
vaccination, using hashtags expressing they do not want to get vaccinated
(e.g., NoVaccination) or are againts mandatory vaccination (e.g..
NoMandatoryVaccine). The hashtags that represent this group are
#EuNãoVouTomarVacina, #NãoVouTomarVacina, #VacinaNão and
#VacinaObrigatóriaNão;

  * -

Anti-sinovaxxers: among the anti-vaxxers, we found opinions specifically
against Coronavac. The hashtags involve derogatory expressions related to
China (e.g. “vacchina”) and are consistent with the contempt display by
Bolsonaro’s in regards to Doria’s effort for an immunization program. Thus, we
decided to create a specific group to analyze whether the anti-stance
represented in this group differs from the general anti-vaccine stance of the
Anti-vaxxers. The hashtags are #VachinaNão, #VachinaNãoPresidente,
#VachinaObrigatóriaNão and #VacinaChinesaNão;

  * -

Neutrals: to represent this group, we selected tweets with “vaccine” or
“vaccination”, no hashtags, and no mentions. The volume collected at random to
represent this group was the average number of tweets of the other groups.

Table 1 displays the volume of collected and preprocessed tweets and the
respective number of users. We crawled the data using the library snscrape
API111https://pypi.org/project/snscrape/. We excluded bots identified using
the API Botometer222https://rapidapi.com/OSoMe/api/botometer-pro, as well as
suspended users. We discarded tweets with less than three terms.

We applied classic pre-processing actions for topic modeling, such as case
normalization and removal of stop words, punctuation, special characters,
hashtags, URLs, and user mentions.

### Topic Modeling

To investigate if there is a difference in the topics discussed by each group
(Q1), we combined two topic modeling techniques: Latent Dirichlet Allocation
(LDA) (?) and BERTopic (?). We regard these techniques as complementary since
LDA provides a coarse-grained clustering based on probabilities of words co-
occurrence in a documents corpus, and BERTopic helps to identify frequent
similar arguments based on tweet similarity. Finding topics at two granularity
levels enables us to leverage the strengths of each technique while reducing
their drawbacks. LDA divides the groups in terms of general concerns, and this
reduced search space enables us to identify representative arguments used to
support the stances using BERTopic.

The LDA input is the corpus and a parameter $k$ (number of topics to
discover). The output is a set of $k$ topics, consisting of terms and
probabilities. To evaluate the results, we used the metric CV (?), which
measures the coherence of topics based on multiple dimensions. We applied LDA
on the set of pre-processed tweets for each group. We varied $k$ from 1 to 30
to find the best $k$ for each case, selecting the ones with the best CV
values. Then, we manually inspected the results using the most representative
terms for each topic and a sample of associated tweets. The $k$ chosen for
each group represents the smallest set of topics found with a coherent set of
terms and the least redundancy topics. Using
Gensim333https://pypi.org/project/gensim/ ($alpha=0.5$, $beta=auto$), we chose
$k=3$ for Anti-sinovaxxers, $k=4$ for Anti-vaxxers and $k=5$ for Neutrals and
Pro-vaxxers.

BERTopic is a framework encompassing algorithms to automatically seek dense
topics in a collection of documents, assuming that semantically similar
documents form topics within the input collection. It requires the input of a
corpus and a pre-trained language representation model (e.g., BERT). After
dimensionality reduction, it finds dense areas of similar documents in the
vector space using HDBScan, a density-based clustering algorithm. Unlike LDA,
BERTopic does not require as input the number of clusters. However, it has as
a drawback the huge number of resulting clusters, which jeopardizes the
interpretation of their semantics.

We propose to find the most representative arguments for each one of the
topics found using LDA. We input to BERTopic the set of documents associated
to a given LDA topic and find clusters of similar tweets. To identify the most
representative tweet of each cluster, we search for the tweet with the highest
average similarity with regard to all tweets of the same cluster. We analyzed
the three largest clusters for each topic of each group. We used BERTopic
library444https://pypi.org/project/bertopic/0.3.4/ and the ‘distiluse-base-
multilingual-cased’555https://huggingface.co/distilbert-base-multilingual-
cased, a BERT trained model that supports the use of 50 different languages.

### Political Polarization Index

To investigate if users are politically polarized (Q2), we propose an index to
measure the political polarization of the users according to the right/left
politicians they follow. For each user, we collect the list of users followed
(followings). Then, we calculate the ratio between the number of followed
right-oriented politicians and the total number of politicians followed (right
or left-oriented). We adopted an offset of 1 for each side to adjust the
calculation when a user does not follow right or left politicians. Thus, the
value 50% indicates politically neutral users, i.e., either do not follow
politicians or follow them in equal amounts. The higher (or lower) the metric
value, the more oriented to the right (left) the user is.

We adopted the Ideological GPS666http://temas.folha.uol.com.br/gps-ideologico/
(iGPS) to select the right/left-oriented politicians. The Ideological GPS is a
tool that adapts the well-known statistical model proposed by Barberá et al.
(?), which calculates political polarization using the followers’ structure.
The iGPS analyzed political influencers, such as artists, politicians,
journalists, etc. Figure 2 shows the position of Brazilian presidents in the
iGPS, such as Lula, Dilma Roussef, Fernando Henrique Cardoso (FHC), and Jair
Bolsonaro. The figure also shows influential politicians who appeared in our
study. Note that the left-oriented politicians, represented in red, are
separated from the right-oriented politicians (represented in green) by a
neutral zone, depicting moderate (neutral) influencers. For example, FHC is
regarded as a moderate who has echo on both sides of the spectrum. We selected
the 165 most left-oriented politicians and the 165 most right-oriented ones,
which corresponds to the left/right-most politicians outside the borders of
the neutral zone.

### Polarization Influence in the Social Network Structure

We analyzed the influence of polarization in the social network structure (Q3)
based on the structural relationships defined by the following list. For each
group, we constructed a directed graph where the nodes correspond to users
from the group or followed by them, and directed edges connect nodes according
to the followings list. First, we calculated global measures for each network
to characterize their complexity, such as degree, average shortest path,
diameter, and clustering coefficient. Next, we detected communities (i.e.,
subgroups) within each network using the Gephi software777https://gephi.org/.
For each community, we calculated the same metrics, as well as closeness
centrality (i.e., how close a node is to others) and the betweenness
centrality (i.e., quantifying the number of times a node acts as a bridge for
other pairs of nodes).

Finally, we assessed the political polarization of the identified communities
using the same list of politicians adopted for the polarization index. We
identified two polarized communities for each group, which essentially
included politicians of a single political orientation (i.e., only right or
left), with a few exceptions. All the other communities did not include
politicians (or virtually none). For these polarized communities, we inspected
the strengths of the connections between different users using subgraphs that
connected them. We examined the connections within graphs containing
politicians only (left and right) and connections between subgraphs containing
regular users connected to politicians (left and right). In this way, we can
evaluate the political influence in the spread of information in each
community.

Since the software used could not handle the size of the two biggest graphs,
corresponding to the Anti-vaxxers and Pro-vaxxers, we adopted the following
sampling method. For the Anti-vaxxers, we randomly divided the group into
three folds of users and constructed the respective graphs three times using
pairs of folds. For each graph, we calculated all metrics and identified the
communities. Then, we compared the results of the three graphs. The graphs
varied in terms of the number of nodes (1.4M-1.55M) and edges (5M-5.5M) but
were very similar in terms of communities found (average 44, STD=1.73). In two
samples, we identified polarized communities with similar properties, with
identical centrality nodes for the polarized communities. For the Pro-vaxxers,
we applied the same sampling method, but due to the amount of data, we divided
the data into ten folds, repeating the process five times. The graphs varied
in terms of the number of nodes (1.8M-2.1M) and edges (5.4M-5.98M) and were
somewhat similar in terms of communities (average 74.6, STD=8). However, we
found similar polarized communities in 4 graphs, both in terms of topological
metrics and centrality nodes. Thus we selected the samples that yielded the
graphs with the most similar topological metrics, including a similar number
of left/right politicians in the polarized communities and the same centrality
nodes.

### Information Sources

To analyze if there exist differences in the source of information exchanged
by the groups (Q4), we used the URLs and mentions in the tweets. Links
represent the sources of information used to ground an opinion or spread
information to other group members. A mention in a tweet represents that
another person’s opinion is relevant to the discussion. This importance may
translate on the spread, endorsement, discussion, or opposition of/to other
people’s points of view.

We divided the URLs into three subcategories: verified news portals, social
network addresses, and others. We classified the news portals using the
indexed list available in Kadaza888https://www.kadaza.com.br/noticias, which
contains 21 recognized news outlets. We also inspected the list of the top-30
most frequent URLs referenced in the tweets, looking for neutral news outlets.
Although some URLs did correspond to widely well-known news websites, all of
them were associated with a clear far-right/left point of view. For
neutrality, we did not classify them as news portals. Addresses related to
social networks were classified as Instagram, Facebook, and Youtube. We did
not found significant references to fact-checking sites, and thus we
disregarded this category. The analysis did not consider all other websites.

Mentions were classified according to group membership: Anti-vaxxers, Anti-
sinovaxxers, and Pro-vaxxers. We also identified users as left/right
politicians from our ideological GPS list. To avoid bias, we identified the
intersection of users in distinct groups, finding 851 common users between the
Anti-sinovaxxers and Pro-vaxxers and 1768 common users between the Anti-
sinovaxxers and Anti-vaxxers. The intersection of the three groups has 340
users. In the analysis of mentions, each user’s behavior is considered only
once (e.g., in the analysis of Anti-vaxxers, a user belonging to more than one
group is regarded only as Anti-vaxxer).

## Results

### Q1: Is there a difference in each group’s topics discussed?

Table 2: Topics per group

Top. | Tweets | Users | Dens. | BT Cl. | Anti-vaxxers: Words
---|---|---|---|---|---
0 | 25,9% | 40,6% | 1,34 | 164 | body, rules, respect, want
1 | 21,8% | 28,9% | 1,58 | 147 | vaccine, lab rat, liberty, Bolsonaro
2 | 20,2% | 25,4% | 1,66 | 133 | get, point, final, dictators
3 | 32,1% | 36,6% | 1,84 | 173 | Brazil, vaccine, people, Doria
Top. | Tweets | Users | Dens. | BT Cl. | Anti-sinovaxxers: Words
0 | 30,9% | 40,1% | 1,94 | 113 |  | doria, dictator, china, want
---
1 | 29,9% | 47,7% | 1,58 | 104 | china, get, this, brazil
2 | 39,1% | 48,8% | 2,01 | 155 | vaccine, chinese, president, against
Top. | Tweets | Users | Dens. | BT Cl. | Pro-vaxxers: Words
0 | 18,2% | 18,8% | 1,54 | 510 | brazil, approved, people, vaccine
1 | 16,8% | 16,9% | 1,57 | 467 | ready, get, waiting, alligator
2 | 18,8% | 15,6% | 1,92 | 485 | science, bolsonaro, out, vaccination
3 | 19,9% | 16% | 1,98 | 508 | life, happy, vaccinated, let’s go
4 | 26,3% | 18,4% | 2,28 | 665 | coronavac, anvisa, today, first
Top. | Tweets | Users | Dens. | BT Cl. | Neutrals: Words
0 | 24,2% | 25% | 1,03 | 534 | covid, where, get, want
1 | 22,5% | 23,5% | 1,01 | 485 | when, take, arm, to take
2 | 19,8% | 20,5% | 1,02 | 454 | against, chinese, can, tomorrow
3 | 18,5% | 19,4% | 1,01 | 467 | want, TGIF, walk, soon
4 | 14,9% | 15,6% | 1,01 | 412 | cure, god, invest, cheap

Table 3: Anti-vaxxers: three largest clusters

Top. | Clust. | #Tw. | Representative Argument
---|---|---|---
| A00 | 155 | “Brazil is going to show that we are not
|  |  | STF’s puppets”
0 | A01 | 131 | “My body, my choice”
| A02 | 141 | “Mandatory vaccination my @#!”
| A10 | 138 | “Bolsonaro always”
1 | A11 | 114 | “All of Brazil in Brasilia”
| A12 | 91 | “Noone is obliging to to take this s#!@”
| A20 | 250 | “Great, that’s what natural selection is for”
2 | A21 | 215 | “Wake up Brazil”
| A22 | 118 | “I raise a prayer to God amem”
| A30 | 636 | “NO to mandatory vaccination, we will not be
|  |  | China’s lab rats”
3 | A31 | 281 | “Let’s do as the people from Buzios and raid Brasilia”
| A32 | 253 | “Let them be lab rats”

Table 4: Anti-sinovaxxers: three largest clusters

Top. | Clust. | #Tw. | Representative Argument
---|---|---|---
| S00 | 409 | “Doria dictatorship. Your corruption vaccine
|  |  | won’t work on me”
0 | S01 | 251 | “We’ll show that we won’t get vaccinated with
|  |  | chinese vaccines, we have the power to fight it”
| S02 | 147 | “Bolsonaro: best president in the history of Brazil”
| S10 | 518 | “I will never take Doria’s vaccine”
1 | S11 | 212 | “No, China never ever”
| S12 | 88 | “Man, you have a lot of courage to deny a vaccine
|  |  | because it’s from China. I’m already waiting
|  |  | in line even if it comes from Chernobyl”
| S20 | 228 | “Say no to the Chinese vaccine Doria wants to force on
|  |  | São Paulo’s people”
2 | S21 | 154 | “Brazilians are not lab rats”
| S22 | 100 | “You guys can have these vaccines made in such a rush”

Table 5: Pro-vaxxers: three largest clusters

Top. | Clust. | #Tw. | Representative Argument
---|---|---|---
| P00 | 483 | “Cheers to SUS, cheers to scientists, cheers to
|  |  | Butantan, cheers to Fiocruz, cheers to the vaccine”
0 | P01 | 425 | “Butantan is serving Brazil, working to save lives”
| P02 | 326 | “I waited so long for this moment”
| P10 | 757 | “Brazil, here comes the vaccine”
1 | P11 | 538 | “I am so happy, so much joy, vaccines have come”
| P12 | 339 | “I am ready to get any vaccine”
| P20 | 467 | “I will get vaccinated for myself and for
|  |  | all Brazilians”
2 | P21 | 374 | “Until when will we pay for bolsonarist
|  |  | incompetence? Vaccines for all”
| P22 | 319 | “It’s revolting to have someone so unprepared
|  |  | as president”
| P30 | 534 | “The vaccines vaccines in Brazilian soil”
3 | P31 | 405 | “Has anyone seen Bolsonaro? He vanished after
|  |  | they announced vaccine approval”
| P32 | 321 | “You garbage, you are a disgrace to this country,
|  |  | you support genocide”
| P40 | 969 | “My greatest wish today: urgent vaccines for all
|  |  | the population so schools can open again”
4 | P41 | 618 | “I want to get vaccinated, I want to be free from the
|  |  | threat of ’coronga’ so I can fight against the threat
|  |  | of Bolsonaro”
| P42 | 300 | “And so the plan of parliamentary immunity and
|  |  | genocide goes on in Brazil”

Table 6: Neutrals: largest cluster

Top. | Clust. | #Tw. | Representative Argument
---|---|---|---
0 | N00 | 356 | “Where is the vaccine?”
1 | N10 | 223 | “Brazil has 2.4 million people vaccinated.
|  |  | USA vaccine 2 million PER DAY.”
2 | N20 | 339 | “Most at-risk group, without mask and
|  |  | crowding, can get out without vaccine”
3 | N30 | 299 | “Am I living or just waiting for the
|  |  | vaccine? TGIF”
4 | N40 | 312 | “When the vaccine is available to you,
|  |  | VACCINE!”

We analyzed the topics discussed by each group and the representative tweets
of their arguments to express their point of view. We elaborate the following
hypotheses: a) the concerns expressed by Anti-vaxxers, Anti-sinovaxxers, and
Pro-vaxxers groups go beyond expressing stance about vaccination, embedding a
significant political bias; b) Anti-sinovaxxers and Anti-vaxxers represent
different anti-vaccination standpoints.

Table 2 shows the LDA topics identified for each group, described by the
percentage of tweets and users, density (tweets/user), number of BERTopic
clusters found, and the four most representative words (highest weight). We
interpreted each topic’s meaning by inspecting the top-50 influential words
and the manual inspection of a sample of related tweets. Then, we further
refined this interpretation using the central arguments of BERTopic clusters.
Tables 3, 4 and 5 present the clusters of semantically similar arguments for
the Anti-vaxxers, Anti-sinovaxxers and Pro-vaxxers. For the three biggest
clusters of each topic, the tables summarize the number of tweets and provide
a representative tweet (i.e., greatest similarity to the other tweets of the
same cluster). For each group, we provide as example in figures 3, 4 and 5 the
similarity matrix of the clusters identified for one topic (Anti-vaxxers,
Anti-sinovaxxers, and Pro-vaxxers, respectively). Table 6 presents
representative arguments for the Neutrals group for the largest cluster. In
the remaining of this section, we describe the results for each group and
discuss our results.

Figure 3: Similarity Matrix - Topic 0 (Anti-vaxxers) Figure 4: Similarity
Matrix - Topic 0 (Anti-sinovaxxers) Figure 5: Similarity Matrix - Topic 0
(Pro-vaxxers)

Anti-vaxxers.  the topics in Table 2 reveal that users in this group are
concerned about their freedom regarding vaccines that they do not trust, as
summarized by the representative arguments in Table 3. To understand the
central arguments of this group, it is necessary to highlight the conflict
between the President and the governors. While the former repeatedly spread in
social media contempt about vaccination, many governors (including Doria)
defended that mandatory vaccination is required in Brazil for sanitary
reasons, raising an inflamed debate about constitutionality. By December 2020,
the Federal Supreme Court (STF) ruled that mandatory vaccination is
constitutional. Topic 0 addresses issues related to mandatory vaccination. The
main arguments are the complaints regarding the STF ruling about mandatory
vaccination (A00), the mantra “my body, my choice” (A01), and swearing about
mandatory vaccination (A2). The similarity matrix comparing these clusters is
displayed in Figure 3. Topic 1 questions the safety of vaccines approved in
such a short time. The main arguments are praises to the President (A10),
organization of protests in Brasilia against the STF (A11), and outrage
against mandatory vaccination (A12). Topic 2 expresses discontent about what
is perceived as authoritarianism (i.e., the STF ruling and the pressure for
mandatory vaccination by governors). Interestingly, the biggest cluster (A20)
endorses vaccination by reminding those who are not willing to get vaccinated
is subject to Darwin’s natural selection, which actually reveals a heated
discussion between anti/pro-vaxxers. The other representative arguments raise
awareness about the STF/governor’s dictatorship (A21) and praise for God’s
protection to the President (A22). Finally, Topic 3 addresses the rivalry with
Doria and the right to freedom guaranteed by the Constitution. The most
representative argument is that people should not be lab rats for the “Chinese
vaccine” (A30), manifestations against either the mobility restrictions or
mandatory vaccination (A31), and insults to those who want to impose the
mandatory vaccination (A32). Topic 3 presents the largest number of tweets and
the highest density (tweets/user), showing a coordinated attempt to spread
specific criticisms about the “Chinese vaccine”. Topic 1 is related to the
largest number of users, revealing that their central concern is mandatory
vaccination.

Anti-sinovaxxers. The topics in Table 2, together with their representative
arguments in Table 4, confirm that the focus of this group is not only on the
vaccination itself, but also the rivalry between Bolsonaro and Doria. The
background behind this group’s discussions is the political dispute for the
2022 Presidential election. As a prospective candidate, Doria leveraged the
wealth of his state to support a joint effort between Instituto Butantan and
the Chinese Pharmaceutical Sinovac to produce Coronavac and promoted this
state program with a discourse that blends science and political interests. In
response, Bolsonaro and his supporters took a stand against the Chinese origin
of Coronavac, summoning xenophobic feelings and questioning Doria’s real
intentions. Topic 0 raises suspicions about the production of Coronavac and
the actual intentions of Governor Doria. The largest cluster of this topic
(S00) presents arguments that raise corruption suspicions connecting Coronavac
and Governor Doria. The cluster S01 calls for protests against the “Chinese
vaccine”, and the cluster S02 praises President Bolsonaro. Since BERTopic
deploys a density-based clustering algorithm, we can see in Figure 4 a blurred
frontier between the arguments of clusters S00 and S01, while is S02
significantly distinct. Topic 1 highlights possible side effects of the
“Chinese vaccine” to support and reaffirm his rejection to Coronavac. However,
we identified an interesting situation: while the arguments of the two biggest
clusters in this topic denigrates the “Chinese origins” of Coronavac, the
representative argument of the third-largest cluster (S12) refutes the ones
used by anti-sinovaxxers. Topic 2 challenges the mandatory COVID-19
immunization in different states, arguing about the speed and safety of
Coronavac’s development. The largest cluster of arguments (S20) refutes the
proposition of mandatory state-wide vaccination in São Paulo. The
representative argument in cluster S21 is that Brazilians cannot be “human
guinea pigs” for a vaccine. In cluster S22, some doubts are thrown on the
timely development of Coronavac regarding the allegedly brief testing period.
Topic 2 presents the highest number of tweets, associated users, and density,
showing that the group’s main concern is to challenge the obligation of a
vaccine, based on a mixture of distrust of it origins and the real intentions
of João Doria.

Pro-vaxxers. According to the topics in Table 2, users in this group are
anxious to get immunized, criticize the federal government (and the
President), and celebrate the results from Brazilian research centers in the
development of a COVID vaccine and its approval by regulatory agencies. To
understand the representative arguments in Table 5, it is necessary to explain
the right-wing agenda being implemented under Bolsonaro’s government. He
defends a frugal Government, proposing the privatization of several federal
agencies (including health-related ones) and drastically reducing universities
and research budgets. He repeatedly insisted on a federal strategy for
COVID-19 management centered on scientifically-refuted “early treatments”
(e.g., chloroquine) and promoted vaccine FUD (Fear, Uncertainty, and Doubt),
particularly regarding Coronavac. For instance, he claims that due to
insufficient testing, Coronavac could transform people into
alligators999https://thewire.in/world/jair-bolsonaro-brazil-coronavirus-
vaccine-anxiety. Topic 0 concentrates comments on the initial milestones of
vaccination in Brazil, congratulating research institutes Fiocruz and
Butantan, which started the production of vaccines in the national territory.
The two largest clusters of arguments (P00 and P01) exalt all institutions
involved with vaccination, while cluster P02 shows expectations with the
beginning of the immunization. Topic 1 addresses the enthusiasm about the
beginning and new stages of the vaccination campaign against COVID, and all
the arguments convey joy, excitement, and hope. The arguments of the two
largest clusters (P10 and P11) celebrate the availability of vaccines in
Brazil, while cluster P12 shows the expectation of users to receive the
vaccine, regardless of its origin. Topic 2 exalts the importance of science
and criticizes the President’s denialism. While the largest cluster (P20)
concentrates on people wishing to receive the vaccine as soon as possible, the
other clusters heavily criticize the president. The clusters P21 and P22
expose the President’s incompetence in leading the pandemic, penalizing the
population with a low supply of vaccines and consequently a prolonged crisis
in health and economy. Topic 3 presents expectations for the availability of
vaccines in Brazil, as well as criticisms to the president and his supporters,
who allegedly do not make efforts to make vaccination a reality for
Brazilians. The representative argument of the largest cluster on this topic
(P30) is happiness about the availability of vaccines in Brazil. In contrast,
clusters P31 and P32 criticize the government for making decisions seen as
contrary to the fight against COVID. Cluster P31 makes fun of Bolsonaro as he
claims to have supported vaccination since the early beginning, and cluster
P32 criticizes the President’s supporters with references to genocide. Topic 4
celebrates the beginning of vaccination in Brazil as a means to return to the
pre-pandemic routine, in addition to criticizing the president for the actions
that culminated in high levels of deaths in Brazil without any political
consequences. The largest cluster on the topic (P40) expresses the expectation
of vaccination for the return of schools, while the other clusters claim for
vaccination to organize raids against the president (P41) and draw attention
to the government’s political armor, regardless of the careless actions of the
President (P42). Topic 4 has the highest number of tweets and density,
revealing that Brazilians are eager to get vaccinated to go back to their
routine. Topic 0, which involves the largest number of users, corroborates
this feeling by spreading the news on the beginning of vaccination.

Neutrals.  The topics identified in the Neutrals group (Table 2) reveal they
are unrest with the availability of vaccination in order to return to a normal
routine without confinement. Table 6 shows the main arguments related to these
topics considering the largest clusters. It is clear they convey the same
expectations regarding the vaccination, but we did not find any reference to
the government/President neither in the form of endorsement or criticism.
Topic 0 questions the delay in the availability of a vaccine, using sentences
such as “Vaccine wanted” and “Where is the vaccine?” (N00). Topic 1 gathers
tweets about the COVID vaccination status around the world and comparisons
with the Brazilian situation. The largest cluster of arguments on this topic
makes comparisons with countries with well-defined immunization plans and are
executing them (N10). Topic 2 shows a concern with the careless behavior of
the population regarding masks and agglomerations. The prevalent argument is
criticisms to people who participate in agglomerations, spread misinformation
about the vaccine, or argue against vaccines’ effectiveness (N20), but with no
direct reference to the President, who often promotes these behaviors. Topic 3
comments on leisure and celebration activities when the pandemic is
controlled, and the most common argument makes references to parties on the
weekend (e.g., TGIF) (N30). Topic 4 reveals concerns surrounding the Brazilian
immunization plan, such as which states will immunize first. The most
representative arguments question when a vaccination will start in Brazil and
its states (N40) and praise the Brazilian Unified Public Health System (SUS),
responsible for the logistics, organization, and application of vaccines.
Topic 0 has the highest density, number of users, and tweets, showing this
group is engaged in expressing immunization haste.

Discussion.  The analysis of the topics and representative arguments enabled
us to confirm our two hypotheses. First, except for the Neutrals group, all
other groups intertwine their anti/pro stance with regard to vaccination with
strong arguments revealing a political stance pro/anti the federal government
and the President. Considering the arguments, we regard both Pro-vaxxers and
Neutrals as pro-vaccination: they want to get vaccinated as soon as possible,
regard immunization as the only means to return to a normal routine and
criticize people’s careless behavior. The Pro-vaxxers’ distinctive behavior is
criticisms of the President and federal government’s actions and the
celebration and expectation involving the beginning of vaccination against
COVID-19 in Brazil. Anti-vaxxers and Anti-sinovaxxers are anti-vaccination:
they do not trust vaccines without extensive research into the efficacy and
side effects and consider vaccination to be an individual choice. We also
confirmed that there is a difference between the Anti-vaxxers and the Anti-
sinovaxxers. While the Anti-vaxxers explain their reasons for being skeptical
and question the mandatory vaccination, the distinctive characteristic of the
Anti-sinovaxxers is the conspiratorial claims behind the interest in a
relationship with a communist country such as China and the rivalry between
Doria and Bolsonaro.

### Q2: Are these groups politically polarized?

Based on the proposed polarization index, we measured the political
orientation of users belonging to the four groups. We elaborate the following
hypotheses: a) the anti-vaccination stance represented by Anti-vaxxers and
Anti-sinovaxxers is in essence expressed by right-oriented users are; b) the
Pro-vaxxers group is composed of users who in the majority are left-oriented;
c) despite its pro-vaccination stance, the Neutrals group is not politically
influenced.

The boxplot in Figure 6 shows the distribution of the political polarization
index within each group in terms of quartiles, medians, and min/max values. In
each group, we can identify a distinct political polarization trend. We can
confirm that the Neutrals group is politically neutral, as the median is 50,
and all users with polarization index lower/higher than 45/50 are regarded as
outliers.

Figure 6: Distribution of political orientation in groups

Despite the medians of Pro-vaxxers and Anti-vaxxers are similar, the first
(Q1) and third quartiles (Q3) of these distributions reveal trends towards the
left and right, respectively. While 75% of Pro-vaxxers (Q3) display a
polarization index of 50% or lower, this range corresponds to only 50% of the
Anti-vaxxers. The Q1 of Pro-vaxxers is 16.66.

Anti-vaxxers display a mirrored behavior compared to Pro-vaxxers, with 25% of
users concentrated in the range between 92.59 (Q3) and 98.97 (Max). The
average index is 62.41, corroborating the tendency towards the right. Anti-
sinovaxxers show a stronger polarization, given that the median is 82.27, Q3
is 95.83, and Max is 98.98. The average polarization in this group is 71.51%.

Discussion. This analysis enabled us to confirm all our hypotheses. It
quantitatively validated the evidence of political orientation found in the
previous analysis of the topics used to express their views. Although both are
right-oriented, the Anti-sinovaxxers group is more extreme compared to Anti-
vaxxers. We were also able to confirm that Pro-vaxxers are oriented towards
the left, while the Neutrals are indeed politically neutrals. Figure 6 help us
to observe the trend towards the right/left of these groups.

### Q3: Does the polarization affect the social network structure of these
groups?

The previous analyses confirmed that all three groups are politically
polarized. In this section, we aim to understand if this polarization reflects
on their social network structure. Our hypotheses are: a) the more polarized a
group is, the more its topological structure reveals stronger connections and
shorter paths for the information flow, and b) the key influencers of a
network have a political alignment with the respective users.

Graphs and Communities. The left-hand side of Table 7 shows the topological
metrics for the group graphs. The clustering coefficients indicate that all
groups have a reasonable probability of containing communities. Despite the
highest clustering coefficient, the Pro-vaxxers graph has the highest number
of communities, which can be explained by the scarce connections between its
nodes (i.e., the smallest average degree - 5.74). The Anti-sinovaxxers is the
smallest and most densely connected graph (average degree 8.31), yielding a
small quantity of equally densely connected communities. In comparison, the
Anti-vaxxers graph has a slightly larger number of communities and a smaller
average degree, but which characterizes it as a more cohesive group compared
to the Pro-vaxxers. Recall that the Anti-vaxxers and Pro-vaxxers graphs
correspond to a sample of the original groups (78,5% and 11,8%, respectively).

Then, we examined each group’s communities, seeking those with more
politicians from our list. We observed that all three groups have two
communities that concentrate a large number of politicians (between 120 and
154): one that concentrates on right-wing politicians and the other, left-wing
politicians. All the other communities do not involve politicians or include a
negligible number. These communities show that polarized users form networks
with other polarized users and that these three groups have politically active
segregated communities.

Polarized Communities. The right-hand side of Table 7 highlights the
properties of each group’s most right/left-polarized communities. The
polarized communities of the Anti-vaxxers have a comparable number of
politicians. However, the right-oriented community is smaller than the left-
oriented one (5 percentage points - pp), significantly more connected (average
degree is 3,6 times larger), with a smaller average shortest path. This
indicates that the right-oriented community has stronger connections. For the
Anti-sinovaxxers, the difference of politicians in the two polarized
communities is slightly bigger (16), but the metrics follow the same pattern:
the right-one polarized community is smaller (12pp), with more edges (10pp),
average degree 3 times larger and smaller shortest average path. The two Pro-
vaxxer communities are slightly different: compared to the right-oriented one,
the left-oriented community has more nodes (12pp) that are equally strongly
connected (average degree is 1.7 larger). It is the polarized community with
the smallest difference of politicians between both sides.

To understand the existence of two polarized communities within each group,
despite their clear right/left orientation, we examined their relationship
with the politicians followed. In the right-oriented Anti-vaxxers and Anti-
sinovaxxers communities, a portion of the right-wing politicians are connected
to each other (44% and 26%, respectively). In the left-oriented communities,
there is no connection between the left-wing politicians. In other words,
these left-wing politicians are followed by anti-vaccination users, while many
of the right-wing politicians are not only followed but are also members of
these communities/groups. In both groups, there is about 2% of users in the
right-oriented communities who are directly connected to right-wing
politicians, with an average degree that is significantly superior compared to
the respective community’s average degree (34.9 and 53.9 for the Anti-
vaxxers/Anti-sinovaxxers, respectively). In these same communities, there is
no connection of users with left-wing politicians. In the left-oriented Anti-
vaxxers and Anti-sinovaxxers communities, the politicians are not connected at
all (right or left). The fraction of users connected to politicians is also
very small, ranging from 0 to 0.4%. Thus, we conclude that the left-oriented
communities in these anti-vaccination groups are composed of users motivated
to refute ideas from other groups, including left-oriented politicians. This
finding is compatible with the arguments found in S12 and A20, which actually
refute the anti-vaccination stance.

The Pro-vaxxers communities display similar trends but with different
intensities, as not all right-wing partisans are against vaccination.
Considering the left-oriented community, the politicians of both sides are
connected to each other in a significant proportion (90% and 29% for
left/right-wing, respectively). There is 0.5% of users connected to right-wing
politicians and 2.2% of users connected with left-oriented ones (average
degrees 3.8 and 10.7, respectively). Thus, the direct connection with left-
oriented politicians is slightly superior compared to the community’s average.
In the right-oriented community, a significant portion of the right-wing
politicians are connected to each other (64%), and 2.4% of users are connected
to them, with an average degree of 9.8. The connections with left-wing
politicians are negligible. There are two possible explanations for the
presence of right-wing users and politicians in the pro-vaccine debates:
confidence in science against
ideologies101010https://www.cnnbrasil.com.br/nacional/2021/01/23/datafolha-79-dos-
brasileiros-querem-se-vacinar-contra-o-coronavirus, or a shift in Bolsonaro’s
agenda to be seen as the one responsible for vaccination despite his stance in
the past111111https://time.com/5946401/brazil-covid-19-vaccines-bolsonaro/,
for electoral purposes.

Finally, we analyzed the main influencers of these communities, represented by
the nodes with the highest in-degree and centrality values. In general,
politicians are very influential in the right-oriented communities and
mainstream media and science activists in the left-oriented ones. The position
of the political influencers in the iGPS is displayed in Figure 2. The users
with the highest number of followers (in-degree) in right-oriented communities
are politicians (Abraham Weintraub - former Ministry of Education, and General
Heleno - Chief Minister of Institutional Security Office) and a right-wing
journalist (Alexandre Garcia). For the left-polarized communities, in all
groups, the streaming platform Netflix has the highest in-degree.

Considering the highest betweenness centrality, responsible for spreading
information throughout the network, we observed for the right-oriented
polarized communities two active Bolsonaro’s supporters (ananiasfernanda and
NiltonGNeto) and the Minister of Communications (Fabio Faria, responsible for
official government communications). For the left-oriented communities, we
identified an anti-Bolsonaro activist (do_genocida), a science YouTuber
(thabataganga), and a politician (GuilhermeBoulos). Considering the closeness
centrality, responsible for shortening the paths to disseminate information,
the right-oriented communities are related to right-wing figures: a profile
that spread apps currently blocked (redpillados) and two far-right journalists
(allanldsantos and RafaelFontana). For the left-oriented communities, the
three figures are press-related: a media freelancer (mfox_us), a journalist
specialized in the medical area (_FabioReis), and a news portal (g1).

Discussion. Our analysis confirmed the hypothesis that the political
polarization affects the social network structure. The anti-vaccination groups
are composed of highly connected users, with a significant proportion of users
engaged in polarized subcommunities (30% and 34% of the Anti-vaxxers and Anti-
sinovaxxers, respectively). While the right-oriented polarized communities act
as a closed bubble to reflect the echo-chamber, the left-oriented ones are
composed of users pre-disposed to pierce the bubble to bounce ideas. The Pro-
vaxxers display a mirrored pattern, although their network structure is more
heterogeneous and open. The left-oriented polarized community engages
connected politicians of both sides in different proportions, but users
connected to them do not have stronger ties compared to the average users of
the community. Regarding the influencers, politicians do play an active role
in the anti-vaccination communities, but we also identified far-right free
lancer journalists. In the Pro-vaxxers communities, in addition to
politicians, we identified science popularization figures. In regards to our
second hypothesis, users are connected to influencers with similar political
orientation in general. However, the group is also open to refute ideas that
in part are spread by influencers from the opposite political spectrum.

Table 7: Properties of Groups and their Polarized Communities

Groups | Graph Properties | Properties of the Most Polarized Community
---|---|---
#Nodes | #Edges |  | Average
---
Degree
| Clust.
---
Coef.
#Com. | #Nodes | #Edges |  | Avg. Shortest
---
Path
Diam. |  | Average
---
Degree
| Right-Wing
---
Politicians
| Left-Wing
---
Politicians
| Highest
---
In-Degree
| Highest Betw.
---
Centrality
| Highest Closen.
---
Centrality
| Anti-
---
vaxxers
1.558.171 | 5.252.810 | 6,74 | 0,004 | 42 |  | 229.022
---
(14,7%)
| 2.056.847
---
(39,1%)
3.56 | 9 | 17.96 | 150 | 1 | AbrahamWeint | ananiasfernanda | redpillados
|  |  |  |  |  |  | 306.478
---
(19,7%)
| 859.406
---
(16,4%)
4.82 | 13 | 5.60 | 8 | 142 | NetflixBrasil | do_genocida | mfox_us
| Anti-sino
---
vaxxers
1.039.047 | 4.320.372 | 8,31 | 0,007 | 38 |  | 146.407
---
(14,1%)
| 1.004.232
---
(23,2%)
4.33 | 10 | 13.71 | 154 | 4 |  | gen_heleno
---
NiltonGNeto | allanldsantos
|  |  |  |  |  |  | 270.009
---
(26%)
| 605.235
---
(14%)
5.31 | 11 | 4.48 | 2 | 138 |  | NetflixBrasil
---
thabataganga | _FabioReis
| Pro-
---
vaxxers
2.027.338 | 5.803.832 | 5,74 | 0,003 | 63 |  | 108.544
---
(5,3%)
| 270.546
---
(4,7%)
4.57 | 11 | 4.98 | 123 | 1 | alexandregarcia | fabiofaria | RafaelFontana
|  |  |  |  |  |  | 356.366
---
(17,6%)
| 1.510.193
---
(26%)
4.68 | 16 | 8.47 | 24 | 120 | NetflixBrasil | GuilhermeBoulos | g1

### Q4: Is there a difference in the information sources used by each group?

Figure 7: Use of Links per group  Figure 8: Mentions between groups  Figure 9:
Mentions of politicians’ tweets

In this section, we aim to understand the use of the information sources by
each group. Our hypotheses are: a) the anti/pro vaccination groups leverage
different sources of information; b) a higher proportion of mentions to users
of one’s own group characterizes an “echo-chamber” effect; and c) politicians
are more influencial in anti-vaccination groups, and thus these groups tend to
have a higher number of mentions to them.

Figure 7 shows the proportional distribution of the web addresses collected
from the tweets with URLs, according to the categories considered, i.e., news
portals and the three prevalent social media platforms. News portals are the
main information source in all groups, although in different proportions:
15.93% for Pro-vaxxers, 12.63% for the Anti-vaxxers, and 13.87% for the Anti-
sinovaxxers. The sum of social medias has a highers percentage of usage in all
groups compared to news portals: 18.70% for Pro-vaxxers, 19.31% for Anti-
vaxxers and 16.48% for Anti-sinovaxxers. Facebook is the least used platform
in all three groups, YouTube is the most popular platform among the Anti-
vaxxers, and Instagram is the prevalent platform in the Pro-vaxxers and Anti-
sinovaxxers groups.

The proportional mentions per group are depicted in Figure 8, considering the
tweets with mentions of each group. The Pro-vaxxers is the group that has the
highest number of mentions to users of its group (65.12%), followed by Anti-
sinovaxxers (30.35%) and Anti-vaxxers (25.16%). Anti-vaxxers and Anti-
sinovaxxers have a mirrored behavior, where Anti-vaxxers make mentions to
Anti-sinovaxxers (10.42%) and vice-versa (7.59%). This result is expected
since both represent an anti-vaccination stance and express common concerns
about mandatory vaccination. In that sense, mentions to anti-vaccination users
amount to 35,58% among the Anti-vaxxers and 37,94% among the Anti-sinovaxxers.
Notice the proportion of Pro-vaxxers’ mentions to Anti-vaxxers and Anti-
sinovaxxers is very small (0,36% and 0,17%, respectively). However, these
figures should be considered with caution, as the number of users in the Pro-
vaxxers group is 6.4 times bigger compared to the Anti-vaxxers.

Finally, Figure 9 depicts the proportional distribution of tweets from
politicians that are used by users as sources of information and propagated.
It is observed that Anti-sinovaxxers and Anti-vaxxers users propagate tweets
with mentions to right-wing politicians (10.53% and 6.93%, respectively),
while Pro-vaxxers use tweets from left-wing politicians in a smaller
proportion (6,21%). Pro-vaxxers are the ones that most spread information
originated from politicians of the opposite spectrum (1,35% for right-wing,
while Anti-sinovaxxers mention 0,17% and Anti-vaxxers 0,10% for left-wing).

Discussion. Our first hypothesis was not totally confirmed. We observed a
relatively similar usage of news portals among all groups, which is evidence
that they all have some level of concern with the veracity of the content.
Nevertheless, Pro-vaxxers have the highest usage of this type of information
source (2pp and almost 4pp higher than the Anti-sinovaxxers and Anti-vaxxers,
respectively). The overall usage of social media is also similar, but the
groups propagate information in different ways. The Anti-vaxxers have the
highest usage of YouTube, a platform in which all sorts of content are freely
propagated. For instance, the far-right journalists who appear with high
centrality nodes in Table 7 have their own channel on Youtube, and do not work
in the mainstream media.

Regarding the second hypothesis, all groups display an “echo chamber”
behavior, with prevalent mentions to users of the same group. This reveals
they seek to re-enforce convictions among their peers and are not very open to
distinct views. Pro-vaxxers are the ones with the highest proportion of
mentions to users of their own group, but they also propagate the most content
from portal news.

Finally, we confirmed the hypothesis that the anti-vaccination groups are the
ones that most rely on information propagated by (right-wing) politicians,
where the Anti-sinovaxxers have almost double of the proportional mentions
compared to the Anti-vaxxers. Recall that proportional references to Youtube
videos are higher when compared to Pro-vaxxers, reinforcing the hypothesis
that they are highly politically polarized. Among the group, the Pro-vaxxers
are the ones that make most mentions to politicians from a different political
orientation (6.21%, compared to 0.1 and 0.18% of the anti-vaccination groups).

## Threats to Validity

This section discusses the threats to the validity of our study. One of the
main threats is the way the groups were defined. The use of hashtags
121212https://help.twitter.com/en/using-twitter/how-to-use-hashtags for
automatic collection of groups on social networks is a widely used form for
this purpose, however, it can lead to different types of bias. First, the
hashtags may not represent the target population, and we mitigated this risk
by a careful inspection of frequent hashtags. Another risk is that tweets
might be falsely inserted in the context of a hashtag because they refute an
idea represented by the hashtag (false positives). The detailed investigation
of topics revealed this bias was inserted in our users, as revealed by
clusters S12 and S20. However, the amount of users is small within the total
of users that compose each group and should not affect the overall patterns
identified.

Another threat of validity is the selected politicians to represent the
political polarization. We mitigate the risk by using iGPS, which is based on
a widely consolidated statistical model (?). Another threat is users who are
politically active but follow politicians of both sides, and thus are
perceived as neutral. More complex models can be evaluated in the future,
which consider, in addition to the followed politicians, the textual contents
of the tweets as a refinement (?).

Finally, it is common knowledge that the Twitter audience may not represent
characteristics of the general population, especially in analyzes such as
this, which represents a frame of the public of this social network.

## Conclusions and Future Work

This paper investigated the influence of political polarization on the
vaccination stances expressed by Brazilians on Twitter. We used a multi-
dimensional framework that encompasses concerns, measurement of polarization,
social network properties, and information sources. We identified three
polarized groups. Pro/against stances were politically polarized towards the
left/right, respectively, where the anti-sinovaxxers were the most politically
polarized.

Our results contradict studies that did not observe a political bias in anti-
vaccination behavior (?; ?), but our analysis is restricted to the specific
Brazilian COVID scenario. The anti-vaccination groups in Brazil express
concerns beyond the observed hesitation highlighted in international studies
(e.g., (?; ?)). Our analysis provided evidence that the opposing groups have
political motivations, given that two candidates for the 2022 presidential
election are using COVID immunization as an electoral platform.

The Anti-sinovaxxers is the most polarized group.Their concerns relate to the
vaccine’s origin and conspiratorial issues regarding Doria’s political
intentions. Anti-vaxxers and pro-vaxxers have opposed political polarization:
their indexes orbit in opposite directions the neutrality with a similar
distance, and the expressed concerns diverge on the importance of collective
immunization. All groups have two polarized communities, segregated in terms
of right/left politicians followed. In general, while one acts as a closed
bubble to reflect the echo chamber, the other is composed of users pre-
disposed to pierce the bubble to bounce ideas. The right-oriented polarized
communities of the anti-vaccination groups are more densely connected, and in
general, are more influenced by politicians. Pro-vaxxers and Neutrals share
haste for immunization, perceived as the only means to get back to a normal
routine, but pro-vaxxers criticize the government’s actions. An “echo chamber”
effect was observed in all groups, mostly propagating ideas aligned with their
own point of view.

As future work, we want to expand the range of dimensions analyzed by the
framework, such as historical behavior analysis.We also want to expand the
concepts of the dimensions already present, such as information flow in the
groups and exploring new metrics to characterize the social network
communities, and understand the dynamics of each interest group.

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