Abstract
Vaccinations have emerged as one of the key tools to combat the COVID-19 pandemic, reduce infections and to enable safe re-opening of societies. Vaccinating the entire world population is a challenging undertaking and with demand far exceeding supply in the world, it is expected that topics surrounding vaccinations generate a wide array of discussions. Therefore, in this paper, we collect data from Twitter during the early days of the COVID-19 vaccination program and adopt a linguistic approach to better understand and appreciate peoples’ concerns and opinions with regards to the roll out of the vaccines. We begin by studying the term frequencies (i.e., unigrams and bigrams) and observe discussions around vaccination doses, receiving doses, vaccine supply, scheduling appointments and wearing masks as the vaccination efforts get underway. We then adopt a seeded topic modeling approach to automatically identify the main topics of discussion in the tweets and the main issues being discussed in each topic. We observe that our dataset has nine distinct topics. For example, we observe topics related to vaccine distribution, eligibility, scheduling and COVID variants. We then study the sentiment of the tweets with respect to each of the nine topics and observe that the overall sentiment is negative for most of the topics. We only observe a higher percentage of positive sentiment for topics related to obtaining information and schools. Our research lays the foundation to conduct a more fine-grained analysis of the various issues faced by the people as the pandemic recedes over the course of the next few years.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexandra Schofield, MĂĄns Magnusson, L.T., Mimno, D.: Understanding text pre-processing for latent Dirichlet allocation. ACL Workshop for Women in NLP (2017). https://www.cs.cornell.edu/~xanda/winlp2017.pdf
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Detoc, M., Bruel, S., Frappe, P., Tardy, B., Botelho-Nevers, E., Gagneux-Brunon, A.: Intention to participate in a COVID-19 vaccine clinical trial and to get vaccinated against COVID-19 in France during the pandemic. Vaccine 38, 7002–7006 (2020)
Jagadesh Jagarlamudi, H.D., Udupa, R.: Incorporating lexical priors into topic models, pp. 204–213 (2012). https://www.aclweb.org/anthology/E12-1021
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600 (2010)
Kwok, S.W.H., Vadde, S.K., Wang, G.: Tweet topics and sentiments relating to COVID-19 vaccination among Australian twitter users: machine learning analysis. J. Med. Internet Res. 23(5), e26953 (2021)
Morstatter, F., Liu, H.: Discovering, assessing, and mitigating data bias in social media. J. Online Soc. Netw. Media 1, 1–13 (2017)
Sarker, A., Lakamana, S., Hogg-Bremer, W., Xie, A., Al-Garadi, M.A., Yang, Y.C.: Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource. J. Am. Med. Inf. Associ. 27(8), 1310–1315 (2020). https://doi.org/10.1093/jamia/ocaa116
Shanthakumar, S.G., Seetharam, A., Ramesh, A.: Analyzing societal impact of COVID-19: a study during the early days of the pandemic. In: 2020 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp. 852–859. IEEE (2020)
Shanthakumar, S.G., Seetharam, A., Ramesh, A.: Understanding the societal disruption due to COVID-19 via user tweets. In: IEEE Smartcomp 2021. IEEE (2021)
Signorini, A., Segre, A.M., Polgreen, P.M.: The use of twitter to track levels of disease activity and public concern in the U.S. during the influenza a H1N1 pandemic. PLoS One 6, e19467 (2011)
Su, Y., Venkat, A., Yadav, Y., Puglisi, L.B., Fodeh, S.J.: Twitter-based analysis reveals differential COVID-19 concerns across areas with socioeconomic disparities. Comput. Biol. Med. 132, 104336 (2021)
To, Q.G., et al.: Applying machine learning to identify anti-vaccination tweets during the COVID-19 pandemic. Int. J. Environ. Res. Public Health 18, 4069 (2021)
Xue, J., et al.: Twitter discussions and emotions about the COVID-19 pandemic: machine learning approach. J. Med. Internet Res. 22(11), e20550 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Esparza, J., Bejarano, G., Ramesh, A., Seetharam, A. (2021). Understanding the Issues Surrounding COVID-19 Vaccine Roll Out via User Tweets. In: Mohaisen, D., Jin, R. (eds) Computational Data and Social Networks. CSoNet 2021. Lecture Notes in Computer Science(), vol 13116. Springer, Cham. https://doi.org/10.1007/978-3-030-91434-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-91434-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91433-2
Online ISBN: 978-3-030-91434-9
eBook Packages: Computer ScienceComputer Science (R0)