How do people feel about COVID-19 vaccine? An analysis of Twitter polarization
DOI:
https://doi.org/10.51359/2526-7884.2025.267022Keywords:
Vaccine, Covid-19, PolarizationAbstract
This study investigates the polarization of public sentiment on Twitter concerning COVID-19 vaccines versus traditional vaccines, analyzing discourse from 2015 to 2021. Utilizing Linguistic Inquiry and Word Count (LIWC) analysis on a large dataset of tweets, we find significantly more positive sentiment and expressions of collective achievement associated with COVID-19 vaccines compared to traditional vaccines, which conversely elicited higher negative sentiment, risk perception, and death-related discourse. These findings challenge prevailing narratives of generalized vaccine hesitancy, suggesting a distinct affective response to COVID-19 vaccines. We attribute this divergence to a confluence of factors, including the unique psychological context of the pandemic (e.g., rallying effects, anxiety management), demographic characteristics of platform users (e.g., optimism bias among younger cohorts), the influence of selective exposure within online environments, and perceptions shaped by the rapid vaccine development process. The results underscore the critical need for nuanced, context-specific public health communication strategies tailored to address the distinct factors shaping risk-benefit perceptions for different vaccine types within diverse online communities.
References
Arguni, E., Karyanti, M. R., Satari, H. I., & Hadinegoro, S. R. (2021). Diphtheria outbreak in Jakarta and Tangerang, Indonesia: Epidemiological and clinical predictor factors for death. PLoS ONE, 16(2 February), 1–11.
Aslan, S., Kızıloluk, S., & Sert, E. (2023). TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm. Neural Computing and Applications, 1–18.
Auxier, B., & Anderson, M. (2021). Social media use in 2021. Pew Research Center. Retrieved from https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. F. B., ... & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221.
Bode, L., & Vraga, E. K. (2018). See something, say something: Correction of global health misinformation on social media. Health Communication, 33(9), 1131-1140.
Bracha, A., & Brown, D. J. (2012). Affective decision making: A theory of optimism bias. Games and Economic Behavior, 75(1), 67–80.
Broniatowski, D. A., Jamison, A. M., Qi, S., AlKulaib, L., Chen, T., Benton, A., Quinn, S. C., & Dredze, M. (2018). Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate. American Journal of Public Health, 108(10), 1378-1384.
Chung, C. K., & Pennebaker, J. W. (2013). Linguistic Inquiry and Word Count (LIWC). Applied Natural Language Processing, 2015, 206–229.
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2021). The echo chamber effect on social media. Proceedings of he National Academy of Sciences, 118(9), e2023301118.
Dolman, A. J., Fraser, T., Panagopoulos, C., Aldrich, D. P., & Kim, D. (2023). Opposing views: associations of political polarization, political party affiliation, and social trust with COVID-19 vaccination intent and receipt. Journal of Public Health, 45(1), 36–39.
Garimella, V. R. K., & Weber, I. (2017). A long-term analysis of polarization on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 528–531.
Garrett, R. K. (2009). Echo chambers online?: Politically motivated selective exposure among Internet news users. Journal of Computer-Mediated Communication, 14(2), 265-285.
Guidry, J. P., Jin, Y., Orr, C. A., Messner, M., & Meganck, S. L. (2017). Ebola on Instagram and Twitter: How health organizations address the health crisis in their social media engagement. Public Relations Review, 43(3), 477-486.
Henkel, L., Sprengholz, P., Korn, L., Betsch, C., & Böhm, R. (2023). The association between vaccination status identification and societal polarization. Nature Human Behaviour, 7(2), 231–239.
Larson, H. J., Jarrett, C., Eckersberger, E., Smith, D. M. D., & Paterson, P. (2014). Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: A systematic review of published literature, 2007-2012. Vaccine, 32(19), 2150-2159.
Lazer, D., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., ... & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094-1096.
O'Connor, C., & Murphy, M. (2020). Going viral: Doctors must tackle fake news in the Covid-19 pandemic. BMJ, 369, m1587.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063-1064.Sousa, G. J. B., Garces, T. S.,
Cestari, V. R. F., Florêncio, R. S., Moreira, T. M. M., & Pereira, M. L. D. (2020). Mortality and survival of COVID-19. Epidemiology and Infection.
Statista. (2021). Distribution of Twitter users worldwide as of April 2021, by age group. https://www.statista.com/statistics/283119/age-distribution-of-global-twitter-users/
Stephan, W. G., Stephan, C. W., & Gudykunst, W. B. (1999). Anxiety in intergroup relations: A comparison of anxiety/uncertainty management theory and integrated threat theory. International Journal of Intercultural Relations, 23(4), 613–628.
Stroud, N. J. (2014). Selective exposure theories. The Oxford Handbook of Political Communication, February 2022, 1–21.
Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54.
Troiano, G., & Nardi, A. (2021). Vaccine hesitancy in the era of COVID-19. Public Health, 194, 245–251.
Walter, D., Ophir, Y., & Jamieson, K. H. (2020). Russian twitter accounts and the partisan polarization of vaccine discourse, 2015–2017. American Journal of Public Health, 110(5), 715–724.
WHO. (2021). WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/
Xie, L., Wang, D., & Ma, F. (2023). Analysis of individual characteristics influencing user polarization in COVID-19 vaccine hesitancy. Computers in Human Behavior, 107649.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Djonata Schiessl
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Statement
- The authors authorize the publication of the article in the journal.
- The opinions and ideas expressed in articles are the authors' sole responsibility.
- The authors guarantee that the article is not the result of plagiarism. Failure to do so may result in penalties for the situation.
- Editors can adjust the text and formatting to fit the article according to the journal's publication standards.
- The articles published are the property of CBR and are protected by copyright. However, authors can disclose and make their articles available for free.
