Can Twitter give insights into international differences in Covid-19 vaccination? Eight countries´ English tweets to 21 March 2021

Autores/as

DOI:

https://doi.org/10.3145/epi.2021.may.11

Palabras clave:

Covid-19, Coronavirus, Pandemics, Vaccination, Public health, Twitter, Word association thematic analysis, WATA, Countries, International study

Resumen

Vaccination programs may help the world to reduce or eliminate Covid-19. Information about them may help countries to design theirs more effectively, with important benefits for public health. This article investigates whether it is possible to get insights into national vaccination programmes from a quick international comparison of public comments on Twitter. For this, word association thematic analysis (WATA) was applied to English-language vaccine-related tweets from eight countries gathered between 5 December 2020 and 21 March 2021. The method was able to quickly identify multiple international differences. Whilst some were irrelevant, potentially non-trivial differences include differing extents to which non-government scientific experts are important to national vaccination discussions. For example, Ireland seemed to be the only country in which university presidents were widely tweeted about in vaccine discussions. India´s vaccine kindness term #VaccineMaitri was another interesting difference, highlighting the need for international sharing.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Mike Thelwall, University of Wolverhampton

Mike Thelwall is the head of the Statistical Cybermetrics Research Group at the University of Wolverhampton, UK. He has developed a wide range of software for gathering and analysing web data, including hyperlink analysis, sentiment analysis and content analysis for Twitter, YouTube, MySpace, blogs and the web in general.

Citas

Alothali, Eiman; Zaki, Nazar; Mohamed, Elfadil A.; Alashwal, Hany (2018). "Detecting social bots on Twitter: a literature review". In: 2018 International conference on innovations in information technology (IIT), pp. 175-180. Los Alamitos: IEEE Press. https://doi.org/10.1109/INNOVATIONS.2018.8605995

Bagcchi, Sanjeet (2021). "The world´s largest Covid-19 vaccination campaign". The Lancet infectious diseases, v. 21, n. 3, p. 323. https://doi.org/10.1016/S1473-3099(21)00081-5

Bell, Beth P.; Romero, Jose R.; Lee, Grace M. (2020). "Scientific and ethical principles underlying recommendations from the Advisory Committee on Immunization Practices for Covid-19 vaccination implementation". Jama, v. 324, n. 20, pp. 2025-2026. https://doi.org/10.1001/jama.2020.20847

Benjamini, Yoav; Hochberg, Yosef (1995). "Controlling the false discovery rate: a practical and powerful approach to multiple testing". Journal of the Royal Statistical Society: Series B, v. 57, n. 1, pp. 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

Blank, Grant; Dutton, William H.; Lefkowitz, Julia (2019). Perceived threats to privacy online: The Internet in Britain, the Oxford Internet Survey, 2019. September 6. https://doi.org/10.2139/ssrn.3522106

Bonnevie, Erika; Gallegos-Jeffrey, Allison; Goldbarg, Jaclyn; Byrd, Brian; Smyser, Joseph (2020). "Quantifying the rise of vaccine opposition on Twitter during the Covid-19 pandemic". Journal of communication in healthcare, v. 14, n. 1, pp. 12-19. https://doi.org/10.1080/17538068.2020.1858222

Braun, Virginia; Clarke, Victoria (2013). Successful qualitative research: A practical guide for beginners. London, UK: Sage. ISBN: 978 1 847875822

Chopra, Harshita; Vashishtha, Aniket; Pal, Ridam; Tyagi, Ananya; Sethi, Tavpritesh (2021)". Mining trends of Covid-19 vaccine beliefs on Twitter with lexical embeddings". arXiv preprint. https://arxiv.org/abs/2104.01131

DeRoo, Sarah S.; Pudalov, Natalie J.; Fu, Linda Y. (2020). "Planning for a Covid-19 vaccination program". Jama, v. 323, n. 24, pp. 2458-2459. https://doi.org/10.1001/jama.2020.8711

Drieger, Pete (2013). "Semantic network analysis as a method for visual text analytics". Procedia-social and behavioral sciences, v. 79, pp. 4-17. https://doi.org/10.1016/j.sbspro.2013.05.053

Engel-Rebitzer, Eden; Camargo-Stokes, Daniel; Buttenheim, Alison; Purtle, Jonathan; Meisel, Zachary F. (2021). "Changes in legislator vaccine-engagement on Twitter before and after the arrival of the Covid-19 pandemic". Human vaccines & immunotherapeutics, pp. 1-5. https://doi.org/10.1080/21645515.2021.1911216

Esquirol, Bernat; Prignano, Luce; Dí­az-Guilera, Albert; Cozzo, Emanuele (2020). "Characterizing Twitter users behaviour during the Spanish Covid-19 first wave". arXiv preprint. https://arxiv.org/abs/2012.06550

Funk, Carey; Tyson, Alec (2020). "Intent to get a Covid-19 vaccine rises to 60% as confidence in research and development process increases". Pew Research Center. https://www.pewresearch.org/science/2020/12/03/intent-to-get-a-covid-19-vaccine-rises-to-60-as-confidence-in-research-and-development-process-increases

Gao, Qi; Abel, Fabian; Houben, Geert-Jan; Yu, Yong (2012). "A comparative study of users´ microblogging behavior on Sina Weibo and Twitter". In: International conference on user modeling, adaptation, and personalization, pp. 88-101. Berlin, Heidelberg: Springer. ISBN: 978 3 642 31453 7 https://link.springer.com/book/10.1007/978-3-642-31454-4

Hassan-Smith, Zaki; Hanif, Wasim; Khunti, Kamlesh (2020). "Who should be prioritised for Covid-19 vaccines?". Lancet, v. 396, n. 10264, pp. 1732-1733. https://doi.org/10.1016/s0140-6736(20)32224-8

Kennedy, Graeme (2014). An introduction to corpus linguistics. Oxford, UK: Routledge. ISBN: 978 0 582231542

Luo, Linhao; Zhang, Xiaofeng; Yang, Xiaofei; Yang, Weihuang (2020). "Deepbot: a deep neural network based approach for detecting Twitter bots". Materials science and engineering, v. 719, v. 1, 012063. https://doi.org/10.1088/1757-899X/719/1/012063

Malik, Amyn A.; McFadden, SarahAnn M.; Elharake, Jad; Omer, Saad B. (2020). "Determinants of Covid-19 vaccine acceptance in the US". EClinicalMedicine, v. 26, 100495. https://doi.org/10.1016/j.eclinm.2020.100495

McClung, Nancy; Chamberland, Mary; Kinlaw, Kathy; Matthew, Dayna B.; Wallace, Megan; Bell, Beth P. (2020). "The Advisory Committee on Immunization Practices´ ethical principles for allocating initial supplies of Covid-19 vaccine - United States, 2020". Morbidity and mortality weekly report, v. 69, n. 47, pp. 1782-1786. https://doi.org/10.15585/mmwr.mm6947e3

Neuendorf, Kimberly A. (2015). The content analysis guidebook. Oxford, UK: Sage. ISBN: 978 1 412979474

Nuzhath, Tasmiah; Tasnim, Samia; Sanjwal, Rahul-Kumar; Trisha, Nusrat-Fahmida; Rahman, Mariya; Mahmud, Farabi; Arman, Arif; Chakraborty, Susmita; Hossain, Md Mahbub (2020). Covid-19 vaccination hesitancy, misinformation and conspiracy theories on social media: A content analysis of Twitter data. https://osf.io/preprints/socarxiv/vc9jb

Painter, Elizabeth M.; Ussery, Emily N.; Patel, Anita; Hughes, Michelle M.; Zell, Elizabeth R.; Moulia, Danielle L. (2021). "Demographic characteristics of persons vaccinated during the first month of the Covid-19 vaccination program - United States, December 14, 2020 - January 14, 2021". Morbidity and mortality weekly report, v. 70, n. 5, pp. 174-177. https://doi.org/10.15585/mmwr.mm7005e1

Paul, Elise; Steptoe, Andrew; Fancourt, David (2021). "Attitudes towards vaccines and intention to vaccinate against Covid-19: Implications for public health communications". The Lancet regional health-Europe, v. 1, 100012. https://doi.org/10.1016/j.lanepe.2020.100012

Phelan, Alexandra L. (2020). "Covid-19 immunity passports and vaccination certificates: scientific, equitable, and legal challenges". The lancet, v. 395, n. 10237, pp. 1595-1598. https://doi.org/10.1016/S0140-6736(20)31034-5

Puri, Neha; Coomes, Eric A.; Haghbayan, Hourmazd; Gunaratne, Keith (2020). "Social media and vaccine hesitancy: new updates for the era of Covid-19 and globalized infectious diseases". Human vaccines & immunotherapeutics, v. 16, n. 11, pp. 2586-2593. https://doi.org/10.1080/21645515.2020.1780846

Ramage, Daniel; Rosen, Evan; Chuang, Jason; Manning, Christopher D.; McFarland, Daniel A. (2009). "Topic modeling for the social sciences". In: NIPS 2009 Workshop on applications for topic models: Text and beyond, pp. 27-33.

Sah, Ranjit; Shrestha, Sunil; Mehta, Rachana; Sah, Sohan K.; Raaban, Ali A.; Dharma, Kuldeep; Rodrí­guez-Morales, Alfonso J. (2021). "AZD1222 (Covishield) vaccination for Covid-19: experiences, challenges and solutions in Nepal". Travel medicine and infectious disease, v. 40, n. 2, 101989. https://doi.org/10.1016/j.tmaid.2021.101989

Thelwall, Mike (2021). Word association thematic analysis: A social media text exploration strategy. San Rafael, CA: Morgan & Claypool. https://doi.org/10.2200/S01071ED1V01Y202012ICR072

Thelwall, Mike; Makita, Meiko; Mas-Bleda, Amalia; Stuart, Emma (2021). ""˜My ADHD Hellbrain´: A Twitter data science perspective on a behavioural disorder". Journal of data and information science, v. 6, n. 1, pp. 13-34. https://doi.org/10.2478/jdis-2021-0007

Wojcik, Stefan; Hughes, Adam (2019). "Sizing up Twitter users". PEW Research Center. https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users

Descargas

Publicado

2021-05-30

Cómo citar

Thelwall, M. (2021). Can Twitter give insights into international differences in Covid-19 vaccination? Eight countries´ English tweets to 21 March 2021. Profesional De La información Information Professional, 30(3). https://doi.org/10.3145/epi.2021.may.11

Número

Sección

Artí­culos de investigación / Research articles