Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario

Ebeling, Régis; Sáenz, Carlos Abel Córdova; Nobre, Jéferson Campos; Becker, Karin

doi:10.1609/icwsm.v16i1.19281

Cited by 15 publications

(14 citation statements)

References 22 publications

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“…Trust and safety-related topics, for example, side effects, rushed vaccine, featured prominently in the antivax tweets, which supports other work that identified safety and trust (in institutions and governments) as a key hurdle in addressing vaccine hesitancy [18,20,23]. Our stance detection approach also enabled the definitive identification of a set of dual-stance users who contributed a significant volume to both antivax and provax tweets, supporting existing findings [4,26,[119][120][121]133].…”

Section: Comparison With Prior Worksupporting

confidence: 81%

“…A growing body of literature has analyzed social media posts, particularly tweets related to COVID-19 vaccines. Here, we compared our work with 29 significant Twitter studies [2][3][4][5][6][7]18,20,21,[23][24][25][26][119][120][121][122][123][124][125][126][127][128][129][130][131][132][133][134]. Unsupervised methods, such as sentiment analysis and topic modeling, are the most popular methods used to classify and analyze tweets; 11 studies used some combination of sentiment analysis, emotion analysis, and topic modeling [3,6,20,21,24,124,126,128,130,131,134].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

See 1 more Smart Citation

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

Zaidi¹,

Ye²,

Samon³

et al. 2023

J Med Internet Res

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Background Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines. Objective This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets. Methods We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques. Results Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24% antivax and 24,463,708/37,044,507, 66.03% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period. Conclusions This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine–related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity.

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Section: Comparison With Prior Worksupporting

confidence: 81%

Section: Comparison With Prior Workmentioning

confidence: 99%

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

Zaidi¹,

Ye²,

Samon³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…While further research is necessary, such a link has been confirmed in some studies. For example, attitudes toward COVID-19 vaccination in Brazil, a country of strong internal conflicts, have been associated with political polarisation [ 80 ]. Moreover, Brazil is also a country where trust in the government is low [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Factors Shaping Vaccine Attitudes and Behaviours in a Low-Trust Society Based on Structural Equation Modelling—The Case of Poland’s Vaccination Programme against COVID-19

Wróblewski

Meler

Stankowska

et al. 2022

IJERPH

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This study focuses on factors that shape vaccine attitudes and behaviours in the context of a low-trust society. Our analysis focuses on the Polish vaccination programme against COVID-19, primarily on (1) the evaluation of the information campaign, (2) trust in the institutions, (3) trust in other people, (4) attitudes toward vaccine safety and efficacy, (5) attitudes toward restrictions related to vaccination (e.g., restricted access to certain services for unvaccinated persons) and the introduction of mandatory vaccination, (6) the evaluation of the government’s actions during the pandemic, and (7) political preferences. The study was conducted with a sample of 1143 adult residents in Poland (CATI). The explanation of the factors determining the COVID-19 vaccine was based on structural equation modelling (SEM). The model showed that the declared fact of vaccination was largely determined by a positive attitude toward restrictions related to vaccination and trust in vaccines. The formation of the provaccine attitude was to an extent determined by the assessment of the government’s campaign and actions during pandemic. While institutional trust had a positive effect on support for the ruling coalition (0.56), the latter on its own had the opposite effect (−0.61) on the formation of provaccine attitude. In the group who both trust institutions and support the parties currently in power, there are more of those who simultaneously reject the restrictions and mandatory vaccination and remain sceptical about the safety and efficacy of COVID-19 vaccines than those who both trust in the vaccine safety and efficacy and accept the restrictions and mandatory vaccination. This indicates that in the context of strong political polarisation, ideological affiliations may play a greater role in shaping vaccine attitudes and behaviours than institutional trust.

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“…There has been a substantial amount of work studying public behaviour towards COVID-19 vaccines since the beginning of the pandemic. These papers have looked at the issue from various angles, such as, discovering the leading factors behind vaccine hesitancy [2]- [4], [10]- [13], correlation between political polarisation and vaccine stance [14], coordinated behaviour in propagation of misinformation [15], etc. They also have used a broad range of techniques to classify tweets as pro-vax or anti-vax, such as, unsupervised learning methods of sentiment analysis and topic modelling or supervised learning techniques where a subset of tweets, hashtags, or users are labelled by annotators and the labels are used to automatically detect stances for tweets or users.…”

Section: Related Workmentioning

confidence: 99%

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets (Preprint)

Zaidi¹,

Ye²,

Samon³

et al. 2022

Preprint

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BACKGROUND Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic, but also for future pathogen outbreaks. OBJECTIVE This study aims to gain insight into the public discussion about COVID-19 vaccine through analysing relevant tweets posted during the first year of the pandemic. METHODS We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax’ and examine the main topics of discourse using topic modelling techniques. RESULTS While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period. Pro-vax tweets focused mostly on vaccine development, while anti-vax tweets covered a wide range of topics, some of which included genuine concerns, though there was a large dose of falsehoods. A number of topics were common to both stances, though pro- and anti-vax tweets discussed them from opposite viewpoints. Memes and jokes were amongst the most retweeted messages. CONCLUSIONS We did not find any evidence of polarisation and online prevalence of anti-vax discourse, however, targeted countering of falsehoods is important. Future research should examine the role of memes and humour in driving online social media activities.

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Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario

Cited by 15 publications

References 22 publications

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

An Analysis of Factors Shaping Vaccine Attitudes and Behaviours in a Low-Trust Society Based on Structural Equation Modelling—The Case of Poland’s Vaccination Programme against COVID-19

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets (Preprint)

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