Understanding communication about the COVID-19 vaccines: analysis of emergent sentiments and topics of discussion on Twitter during the initial phase of the vaccine rollout

References

• Acheampong, T., Akorsikumah, E. A., Osae-Kwapong, J., Khalid, M., Appiah, A., & Amuasi, J. H. (2021). Examining vaccine hesitancy in Sub-Saharan Africa: A survey of the knowledge and attitudes among adults to receive COVID-19 vaccines in Ghana. Vaccines, 9(8), Article 814. https://doi.org/10.3390/vaccines9080814
   Web of Science ®Google Scholar
• Adil, M. T., Rahman, R., Whitelaw, D., Jain, V., Al-Taan, O., Rashid, F., Munasinghe, A., & Jambulingam, P. (2021). SARS-CoV-2 and the pandemic of COVID-19. Postgraduate Medical Journal, 97(1144), 110–116. https://doi.org/10.1136/postgradmedj-2020-138386
   PubMed Web of Science ®Google Scholar
• Agarwal, A., Romine, W. L., & Banerjee, T. (2020, December). Leveraging natural language processing to understand public outlook towards the influenza vaccination. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 4981–4987). IEEE. https://doi.org/10.1109/BigData50022.2020.9378075.
   Google Scholar
• Aslam, S. (2018). Twitter by the numbers: Stats, demographics & fun facts. Retrieved January 26, 2022, from Omnicoreagency.com
   Google Scholar
• Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
   Web of Science ®Google Scholar
• Bewick, V., Cheek, L., & Ball, J. (2004). Statistics review 10: Further nonparametric methods. Critical Care, 8(3), 196–199. https://doi.org/10.1186/cc2857
   PubMedGoogle Scholar
• Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
   Web of Science ®Google Scholar
• Bråten, I., & Braasch, J. L. G. (2017). Key issues in research on students’ critical reading and learning in the 21st century information society. In C. Ng, & B. Bartlett (Eds.), Improving reading and reading engagement in the 21st century (pp. 77–98). Springer. https://doi.org/10.1007/978-981-10-4331-4_4
   Google Scholar
• Bujang, M. A., Sa’at, N., & Bakar, T. M. I. T. A. (2018). Sample size guidelines for logistic regression from observational studies with large population: Emphasis on the accuracy between statistics and parameters based on real life clinical data. The Malaysian Journal of Medical Sciences: MJMS, 25(4), 122–130. https://doi.org/10.21315/mjms2018.25.4.12
   PubMed Web of Science ®Google Scholar
• CDC. (2021, September 3). How to protect yourself & others. Retrieved September 10, 2021, from https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html
   Google Scholar
• CDC. (2022a, January 5). COVID Data tracker. Retrieved January 05, 2023, from https://covid.cdc.gov/covid-data-tracker
   Google Scholar
• CDC. (2022b, November). COVID data tracker: Rates of COVID-19 cases and deaths by vaccination status. Retrieved January 5, 2023, from https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-staus
   Google Scholar
• Cerda, A. A., & García, L. Y. (2021). Factors explaining the fear of being infected with COVID-19. Health Expectations, 25(2), 506–512.https://doi.org/10.1111/hex.13274
   PubMed Web of Science ®Google Scholar
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
   Web of Science ®Google Scholar
• Chowdhury, N., Khalid, A., & Turin, T. C. (2021). Understanding misinformation infodemic during public health emergencies due to large-scale disease outbreaks: A rapid review. Journal of Public Health, 1–21. https://doi.org/10.1007/s10389-021-01565-3
   PubMed Web of Science ®Google Scholar
• Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10(1), 1. https://doi.org/10.1038/s41598-020-73510-5
   PubMed Web of Science ®Google Scholar
• Collins, A., & Alexander, R. (2022). Reproducibility of COVID-19 pre-prints. Scientometrics, 127(8), 4655–4673. https://doi.org/10.1007/s11192-022-04418-2
   PubMed Web of Science ®Google Scholar
• Cummings, L. (2014). The “trust” heuristic: Arguments from authority in public health. Health Communication, 29(10), 1043–1056. https://doi.org/10.1080/10410236.2013.831685
   PubMed Web of Science ®Google Scholar
• Duncan, R. G., Chinn, C. A., & Barzilai, S. (2018). Grasp of evidence: Problematizing and expanding the next generation science standards’ conceptualization of evidence. Journal of Research in Science Teaching, 55(7), 907–937. https://doi.org/10.1002/tea.21468
   Web of Science ®Google Scholar
• El-Mohandes, A., White, T. M., Wyka, K., Rauh, L., Rabin, K., Kimball, S. H., Ratzan, S. C., & Lazarus, J. V. (2021). COVID-19 vaccine acceptance among adults in four major US metropolitan areas and nationwide. Scientific Reports, 11(1), 21844. https://doi.org/10.1038/s41598-021-00794-6
   PubMed Web of Science ®Google Scholar
• Elsner, J. N., Sadler, T. D., Zangori, L., Friedrichsen, P. J., & Ke, L. (2022). Student interest, concerns, and information-seeking behaviors related to COVID-19. Disciplinary and Interdisciplinary Science Education Research, 4(1), Article 11. https://doi.org/10.1186/s43031-022-00053-2
   PubMedGoogle Scholar
• Fancourt, D., Steptoe, A., & Wright, L. (2020). The Cummings effect: Politics, trust, and behaviours during the COVID-19 pandemic. The Lancet, 396(10249), 464–465. https://doi.org/10.1016/S0140-6736(20)31690-1
   PubMed Web of Science ®Google Scholar
• Forgas, J. P. (2019). Happy believers and sad skeptics? Affective influences on gullibility. Current Directions in Psychological Science, 28(3), 306–313. https://doi.org/10.1177/0963721419834543
   Web of Science ®Google Scholar
• Forgas, J. P., & East, R. (2008). On being happy and gullible: Mood effects on skepticism and the detection of deception. Journal of Experimental Social Psychology, 44(5), 1362–1367. https://doi.org/10.1016/j.jesp.2008.04.010
   Web of Science ®Google Scholar
• Forgie, S. E., Duff, J. P., & Ross, S. (2013). Twelve tips for using Twitter as a learning tool in medical education. Medical Teacher, 35(1), 8–14. https://doi.org/10.3109/0142159X.2012.746448
   PubMed Web of Science ®Google Scholar
• Fraser, N., Brierley, L., Dey, G., Polka, J., Palfy, M., Nanni, F., & Coates, J. (2021). The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape. PLOS Biology, 19(4), e3000959. https://doi.org/10.1371/journal.pbio.3000959
   PubMed Web of Science ®Google Scholar
• Greenhow, C., & Gleason, B. (2012, October). Twitteracy: Tweeting as a new literacy practice. The Educational Forum, 76(4), 464–478. . https://doi.org/10.1080/00131725.2012.709032
   Google Scholar
• Gutmann, A., & Thompson, D. (1997). Deliberating about bioethics. Hastings Center Report, 27(3), 38–41. https://doi.org/10.2307/3528667
   PubMed Web of Science ®Google Scholar
• Hoffman, B. L., Felter, E. M., Chu, K. H., Shensa, A., Hermann, C., Wolynn, T., Williams, D., & Primack, B. A. (2019). It’s not all about autism: The emerging landscape of anti-vaccination sentiment on Facebook. Vaccine, 37(16), 2216–2223. https://doi.org/10.1016/j.vaccine.2019.03.003
   PubMed Web of Science ®Google Scholar
• Hossain, E., Rana, J., Islam, S., Khan, A., Chakrobortty, S., Ema, N. S., & Bekun, F. V. (2021). COVID-19 vaccine-taking hesitancy among Bangladeshi people: Knowledge, perceptions and attitude perspective. Human Vaccines & Immunotherapeutics, 17(11), 4028–4037. https://doi.org/10.1080/21645515.2021.1968215
   PubMed Web of Science ®Google Scholar
• Issanov, A., Akhmetzhanova, Z., Riethmacher, D., & Aljofan, M. (2021). Knowledge, attitude, and practice toward COVID-19 vaccination in Kazakhstan: A cross-sectional study. Human Vaccines & Immunotherapeutics, 17(10), 3394–3400. https://doi.org/10.1080/21645515.2021.1925054
   PubMed Web of Science ®Google Scholar
• Jayaweera, M., Perera, H., Gunawardana, B., & Manatunge, J. (2020). Transmission of COVID-19 virus by droplets and aerosols: A critical review on the unresolved dichotomy. Environmental Research, 188, Article 109819. https://doi.org/10.1016/j.envres.2020.109819
   PubMed Web of Science ®Google Scholar
• Kirk, E., Romine, W., Sadler, T., Elsner, J., Zangori, L., & Ke, L. (In Review). Interest and effort: Exploring the relationship between students COVID-19 interest and information seeking behavior. International Journal of Science Education Part B: Science Communication and Public Engagement.
   Web of Science ®Google Scholar
• Koerber, A. (2020). Is it fake news or is it open science? Science communication in the COVID-19 pandemic. Journal of Business and Technical Communication, 35(1), 22–27. https://doi.org/10.1177/1050651920958506
   Web of Science ®Google Scholar
• Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E., Zarka, J., Traboulsi, C., Akl, E. W., & Baddour, K. (2020). Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on twitter. Cureus, 12(3), e7255. https://doi.org/10.7759/cureus.7255
   PubMed Web of Science ®Google Scholar
• Kruskal, W. H., & Wallis, A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. https://doi.org/10.1080/01621459.1952.10483441
   Web of Science ®Google Scholar
• Kwok, S. W. H., Vadde, S. K., & Wang, G. (2021). Tweet topics and sentiments relating to COVID-19 vaccination among Australian twitter users: Machine learning analysis. Journal of Medical Internet Research, 23(5), Article e26953. https://doi.org/10.2196/26953
   Web of Science ®Google Scholar
• Lederman, N. G., Abd-El-Khalick, F., Bell, R. L., & Schwartz, R. S. (2002). Views of nature of science questionnaire: Toward valid and meaningful assessment of learners’ conceptions of nature of science. Journal of Research in Science Teaching, 39(6), 497–521. https://doi.org/10.1002/tea.10034
   Web of Science ®Google Scholar
• Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5(3), 337–348. https://doi.org/10.1038/s41562-021-01056-1
   PubMed Web of Science ®Google Scholar
• Loureiro, M. L., & Alló, M. (2020). Sensing climate change and energy issues: Sentiment and emotion analysis with social media in the UK and Spain. Energy Policy, 143, Article 111490. https://doi.org/10.1016/j.enpol.2020.111490
   Web of Science ®Google Scholar
• Mahajan, R., Romine, W., Miller, M., & Banerjee, T. (2019). Analyzing public outlook towards vaccination using Twitter. In 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/BigData47090.2019.9006136.
   Google Scholar
• Mamidi, R., Miller, M., Banerjee, T., Romine, W., & Sheth, A. (2019). Identifying key topics bearing negative sentiment on Twitter: insights concerning the 2015-2016 Zika epidemic. JMIR public health and surveillance, 5(2), Article e11036. https://doi.org/10.2196/11036
   PubMedGoogle Scholar
• Manca, S., Bocconi, S., & Gleason, B. (2021). “Think globally, act locally”: A glocal approach to the development of social media literacy. Computers & Education, 160, Article 104025. https://doi.org/10.1016/j.compedu.2020.104025
   Web of Science ®Google Scholar
• MEDLINE Overview. (2021). https://www.nlm.nih.gov/medline/medline_overview.html
   Google Scholar
• Metzger, M. J., Flanagin, A. J., & Zwarun, L. (2003). College student Web use, perceptions of information credibility, and verification behavior. Computers & Education, 41(3), 271–290. https://doi.org/10.1016/S0360-1315(03)00049-6
   Web of Science ®Google Scholar
• Miller, M., Banerjee, T., Muppalla, R., Romine, W., & Sheth, A. (2017). What are people tweeting about Zika? An exploratory study concerning its symptoms, treatment, transmission, and prevention. JMIR Public Health and Surveillance, 3(2), Article e38. https://doi.org/10.2196/publichealth.7157
   PubMedGoogle Scholar
• Nan, X., Iles, I. A., Yang, B., & Ma, Z. (2022). Public health messaging during the COVID-19 pandemic and beyond: Lessons from communication science. Health Communication, 37(1), 1–19. https://doi.org/10.1080/10410236.2021.1994910
   PubMed Web of Science ®Google Scholar
• Neely, S. R., Eldredge, C., Ersing, R., & Remington, C. (2022, January). Vaccine hesitancy and exposure to misinformation: A survey analysis. Journal of General Internal Medicine, 37(1), 179–187. https://doi.org/10.1007/s11606-021-07171-z
   PubMed Web of Science ®Google Scholar
• NRC. (2021). NRC emotion lexicon. Retrieved November 9, 2021, from https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm
   Google Scholar
• Piedrahita-Valdés, H., Piedrahita-Castillo, D., Bermejo-Higuera, J., Guillem-Saiz, P., Bermejo-Higuera, J. R., Guillem-Saiz, J., Sicilia-Montalvo, J. A., & Machío-Regidor, F. (2021). Vaccine hesitancy on social media: Sentiment analysis from June 2011 to April 2019. Vaccines, 9(1), Article 28. https://doi.org/10.3390/vaccines9010028
   Web of Science ®Google Scholar
• Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211–227. https://doi.org/10.1002/sce.3730660207
   Google Scholar
• Puri, N., Coomes, E. A., Haghbayan, H., & Gunaratne, K. (2020). Social media and vaccine hesitancy: New updates for the era of COVID-19 and globalized infectious diseases. Human Vaccines & Immunotherapeutics, 16(11), 2586–2593. https://doi.org/10.1080/21645515.2020.1780846
   PubMed Web of Science ®Google Scholar
• Qin, J., You, C., Lin, Q., Hu, T., Yu, S., & Zhou, X.-H. (2020). Estimation of incubation period distribution of COVID-19 using disease onset forward time: A novel cross-sectional and forward follow-up study. Science Advances, 6(33), eabc1202. https://doi.org/10.1126/sciadv.abc1202
   PubMed Web of Science ®Google Scholar
• Raghupathi, V., Ren, J., & Raghupathi, W. (2020). Studying public perception about vaccination: A sentiment analysis of tweets. International Journal of Environmental Research and Public Health, 17(10), Article 3464. https://doi.org/10.3390/ijerph17103464
   PubMed Web of Science ®Google Scholar
• Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., Hasell, J., Macdonald, B., Beltekian, D., & Roser, M. (2020). Coronavirus pandemic (COVID-19). Our World in Data. https://ourworldindata.org/coronavirus
   Google Scholar
• Rodriguez-Morales, A. J., & Franco, O. H. (2021). Public trust, misinformation and COVID-19 vaccination willingness in Latin America and the Caribbean: Today’s key challenges. The Lancet Regional Health – Americas, 3, Article 100073. https://doi.org/10.1016/j.lana.2021.100073
   PubMedGoogle Scholar
• Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L. J., Recchia, G., van der Bles, A. M., & van der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world. Royal Society Open Science, 7(10), Article 201199. https://doi.org/10.1098/rsos.201199
   PubMed Web of Science ®Google Scholar
• Sattar, N. S., & Arifuzzaman, S. (2021). COVID-19 Vaccination awareness and aftermath: Public sentiment analysis on twitter data and vaccinated population prediction in the USA. Applied Sciences, 11(13), Article 6128. https://doi.org/10.3390/app11136128
   Google Scholar
• Shen, S. C., & Dubey, V. (2019, March). Addressing vaccine hesitancy: Clinical guidance for primary care physicians working with parents. Canadian family physician Medecin de famille canadien. Retrieved June 6, 2022, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515949/
   Google Scholar
• Shi, J., Zeng, X., Cui, P., Yan, C., & Chen, H. (2023). Alarming situation of emerging H5 and H7 avian influenza and effective control strategies. Emerging Microbes & Infections, 12(1), Article 2155072. https://doi.org/10.1080/22221751.2022.2155072
   Web of Science ®Google Scholar
• Siddiqui, M., Salmon, D. A., & Omer, S. B. (2013). Epidemiology of vaccine hesitancy in the United States. Human Vaccines & Immunotherapeutics, 9(12), 2643–2648. https://doi.org/10.4161/hv.27243
   PubMed Web of Science ®Google Scholar
• Silva, T. M., Estrela, M., Roque, V., Gomes, E. R., Figueiras, A., Roque, F., & Herdeiro, M. T. (2022). Perceptions, knowledge and attitudes about COVID-19 vaccine hesitancy in older Portuguese adults. Age and Ageing, 51(3), Article afac013. https://doi.org/10.1093/ageing/afac013
   Web of Science ®Google Scholar
• Singla, Z., Randhawa, S., & Jain, S. (2017, June). Sentiment analysis of customer product reviews using machine learning. In 2017 International Conference on Intelligent Computing and Control (I2C2) (pp. 1–5), IEEE.
   Google Scholar
• Tanne, J. H. (2021). Covid-19: FDA approves Pfizer-BioNTech vaccine in record time. BMJ, 374. https://doi.org/10.1136/bmj.n2096
   Google Scholar
• Tavilani, A., Abbasi, E., Ara, F. K., Darini, A., & Asefy, Z. (2021). COVID-19 vaccines: Current evidence and considerations. Metabolism Open, 12, Article 100124. https://doi.org/10.1016/j.metop.2021.100124
   PubMedGoogle Scholar
• Treagust, D. F., & Duit, R. (2008). Conceptual change: A discussion of theoretical, methodological and practical challenges for science education. Cultural Studies of Science Education, 3(2), 297–328. https://doi.org/10.1007/s11422-008-9090-4
   Google Scholar
• Ullah, I., Khan, K. S., Tahir, M. J., Ahmed, A., & Harapan, H. (2021). Myths and conspiracy theories on vaccines and COVID-19: Potential effect on global vaccine refusals. Vacunas (English Edition), 22(2), 93–97. https://doi.org/10.1016/j.vacune.2021.01.009
   Google Scholar
• Van Atteveldt, W., Van der Velden, M., & Boukes, M. (2021). The validity of sentiment analysis: Comparing manual annotation, crowd-coding, dictionary approaches, and machine learning algorithms. Communication Methods and Measures, 15(2), 121–140. https://doi.org/10.1080/19312458.2020.1869198
   Web of Science ®Google Scholar
• WHO. (2023). WHO Coronavirus (COVID-19) Dashboard. Retrieved January 5, 2023, from https://covid19.who.int/; 2022
   Google Scholar
• Yousefinaghani, S., Dara, R., Mubareka, S., Papadopoulos, A., & Sharif, S. (2021). An analysis of COVID-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases, 108, 256–262. https://doi.org/10.1016/j.ijid.2021.05.059
   PubMed Web of Science ®Google Scholar
• Zaman, A. (2015). Deification of science and its disastrous consequences. International Journal of Pluralism and Economics Education, 6(2), 181–197. https://doi.org/10.1504/IJPEE.2015.072595
   Google Scholar
• Zeng, J., & Schäfer, M. S. (2021). Conceptualizing “dark platforms”. Covid-19-related conspiracy theories on 8kun and Gab. Digital Journalism, 9(9), 1321–1343. https://doi.org/10.1080/21670811.2021.1938165
   Web of Science ®Google Scholar