Skip to main content
SpringerLink
Log in

Analyzing COVID-19 Vaccine Tweets for Tonal Shift

  • Conference paper
  • First Online:
HCI International 2021 - Posters (HCII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1421))

Included in the following conference series:

Abstract

On November 09, 2020, Pfizer and BioNtech announced vaccine efficacy results, possibly providing hope during the COVID-19 pandemic. Correspondingly, vaccine-related information was shared on social media platforms, including Twitter. The present research aims to investigate tonal shift resulting from this important pandemic-related event using automatic text analysis of Twitter Tweets. We examined 209,939 tweets before, and 203,490 tweets after the vaccine announcement. Pennebaker’s linguistic inquiry word count (LIWC) was used to detect tonal shifts via analytic thinking (which reflects logical thinking), clout (reflects expertise), authentic (reflects disclosure), and emotional tone (reflects emotional valence). Results indicated a decrease in authentic score implying a more guarded form of disclosure, while an increase in clout score suggests more sharing from expert users. The change was negligible for analytical thinking and emotional tone, suggesting users’ mentality towards the pandemic was not affected. Overall, results suggest a minimal shift in tone on Twitter, even in the face of the good news about the vaccine announcement.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. WHO Coronavirus Disease (COVID-19) Dashboard. World Health Organization (WHO) (n.d.). https://covid19.who.int. Accessed 8 Mar 2021

  2. WHO’s three messages for UNGA75. World Health Organization (WHO), 15 September 2020. https://www.who.int/news/item/15-09-2020-who-s-three-messages-for-unga75

  3. Business Wire: Pfizer and BioNTech Announce Vaccine Candidate Against COVID-19 Achieved Success in First Interim Analysis from Phase 3 Study, 9 November 2020. https://www.businesswire.com/news/home/20201109005539/en/%C2%A0Pfizer-and-BioNTech-Announce-Vaccine-Candidate-Against-COVID-19-Achieved-Success-in-First-Interim-Analysis-from-Phase-3-Study

  4. Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in Twitter events. J. Am. Soc. Inform. Sci. Technol. 62(2), 406–418 (2011). https://doi.org/10.1002/asi.21462

    Article  Google Scholar 

  5. Pennebaker, J.W., Mehl, M.R., Niederhoffer, K.G.: Psychological aspects of natural language use: our words our selves. Ann. Rev. Psychol. 54(1), 547–577 (2003). https://doi.org/10.1146/annurev.psych.54.101601.145041

    Article  Google Scholar 

  6. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC, vol. 10 (2010)

    Google Scholar 

  7. Scanfeld, D., Scanfeld, V., Larson, E.L.: Dissemination of health information through social networks: Twitter and antibiotics. Am. J. Infect. Control 38(3), 182–188 (2010). https://doi.org/10.1016/j.ajic.2009.11.004

    Article  Google Scholar 

  8. Roth, Y., Pickles, N.: Updating our approach to misleading information, 11 May 2020. https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-information.html

  9. Jones, N.M., Silver, R.C.: This is not a drill: anxiety on Twitter following the 2018 Hawaii false missile alert. Am. Psychol. 75(5), 683–693 (2020). https://doi.org/10.1037/amp0000495

    Article  Google Scholar 

  10. Gascó, M., Bayerl, P.S., Denef, S., Akhgar, B.: What do citizens communicate about during crises? Analyzing Twitter use during the 2011 UK riots. Gov. Inf. Q. 34(4), 635–645 (2017). https://doi.org/10.1016/j.giq.2017.11.005

    Article  Google Scholar 

  11. Choi, D., Kim, P.: Sentiment analysis for tracking breaking events: a case study on Twitter. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013. LNCS (LNAI), vol. 7803, pp. 285–294. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36543-0_30

    Chapter  Google Scholar 

  12. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inform. Sci. Technol. 61(12), 2544–2558 (2010). https://doi.org/10.1002/asi.21416

    Article  Google Scholar 

  13. Xu, W.(Wayne), Zhang, C.: Sentiment, richness, authority, and relevance model of information sharing during social Crises—the case of #MH370 tweets. Comput. Hum. Behav. 89, 199–206 (2018). https://doi.org/10.1016/j.chb.2018.07.041

    Article  Google Scholar 

  14. Chen, E., Lerman, K., Ferrara, E.: Tracking social media discourse about the COVID-19 pandemic: development of a public coronavirus Twitter data set. JMIR Public Health Surveill. 6(2), e19273 (2020). https://doi.org/10.2196/19273

    Article  Google Scholar 

  15. Documenting the Now: Hydrator [Computer Software] (2020). https://github.com/docnow/hydrator

  16. McHugh, M.C., Saperstein, S.L., Gold, R.S.: OMG U #Cyberbully! An exploration of public discourse about cyberbullying on Twitter. Health Educ. Behav. 46(1), 97–105 (2018). https://doi.org/10.1177/1090198118788610

    Article  Google Scholar 

  17. Veltri, G.A., Atanasova, D.: Climate change on Twitter: content, media ecology and information sharing behaviour. Public Underst. Sci. 26(6), 721–737 (2015). https://doi.org/10.1177/0963662515613702

    Article  Google Scholar 

  18. Golder, S.A., Macy, M.W.: Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051), 1878–1881 (2011). https://doi.org/10.1126/science.1202775

    Article  Google Scholar 

  19. Pope, D., Griffith, J.: An analysis of online Twitter sentiment surrounding the European refugee crisis. In: Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 299–306 (2016). https://doi.org/10.5220/0006051902990306

  20. Pennebaker, J.W., Booth, R.J., Boyd, R.L., Francis, M.E.: Linguistic Inquiry and Word Count: LIWC 2015. Pennebaker Conglomerates, Austin (2015). www.LIWC.net

  21. Pennebaker, J.W., Boyd, R., Jordan, K., Blackburn, K.: The development and psychometric properties of LIWC 2015. University of Texas at Austin (2015). https://doi.org/10.15781/T29G6Z

  22. Pennebaker, J.W., Chung, C.K., Frazee, J., Lavergne, G.M., Beaver, D.I.: When small words foretell academic success: the case of college admissions essays. PLoS ONE 9(12), e115844 (2015). https://doi.org/10.1371/journal.pone.0115844

    Article  Google Scholar 

  23. Kacewicz, E., Pennebaker, J.W., Davis, M., Jeon, M., Graesser, A.C.: Pronoun use reflects standings in social hierarchies. J. Lang. Soc. Psychol. 33(2), 125–143 (2013). https://doi.org/10.1177/0261927X13502654

    Article  Google Scholar 

  24. Newman, M.L., Pennebaker, J.W., Berry, D.S., Richards, J.M.: Lying words: predicting deception from linguistic styles. Pers. Soc. Psychol. Bull. 29(5), 665–675 (2003). https://doi.org/10.1177/0146167203029005010

    Article  Google Scholar 

  25. Cohn, M.A., Mehl, M.R., Pennebaker, J.W.: Linguistic markers of psychological change surrounding September 11, 2001. Psychol. Sci. 15(10), 687–693 (2004). https://doi.org/10.1111/j.0956-7976.2004.00741.x

    Article  Google Scholar 

  26. Nguyen, V.H., Claus, E.: Good news, bad news, consumer sentiment and consumption behavior. J. Econ. Psychol. 39, 426–438 (2013). https://doi.org/10.1016/j.joep.2013.10.001

    Article  Google Scholar 

  27. Soroka, S., Fournier, P., Nir, L.: Cross-national evidence of a negativity bias in psychophysiological reactions to news. Proc. Natl. Acad. Sci. U. S. A. 116(38), 18888–18892 (2019). https://doi.org/10.1073/pnas.1908369116

    Article  Google Scholar 

  28. Wallston, K.A.: Cautious optimism vs. cockeyed optimism. Psychol. Health 9(3), 201–203 (1994). https://doi.org/10.1080/08870449408407480

    Article  Google Scholar 

  29. The Economist: The second wave of COVID-19 has sent much of Europe back into lockdown. The Economist, 7 November 2020. https://www.economist.com/briefing/2020/11/07/the-second-wave-of-covid-19-has-sent-much-of-europe-back-into-lockdown

  30. Denmark wants to cull 15 million minks over COVID fears. AP NEWS, 4 November 2020. https://apnews.com/article/denmark-cull-15-million-minks-covid-19-37f57a303bbf738efca50918c35696de

  31. International Update: Global Covid infections pass 51.4 million—100,000 cases per day in US. Pharmaceutical Technology, 11 November 2020. https://www.pharmaceutical-technology.com/special-focus/covid-19/international-update-global-covid-infections-pass-51-4-million-100000-cases-per-day-in-us/

  32. Zheng, H., Goh, D.H.-L., Lee, C.S., Lee, E.W.J., Theng, Y.L.: Uncovering temporal differences in COVID-19 tweets. Proc. Assoc. Inf. Sci. Technol. 57(1), e233 (2020). https://doi.org/10.1002/pra2.233

    Article  Google Scholar 

  33. Hung, M., et al.: Social network analysis of COVID-19 sentiments: application of artificial intelligence. J. Med. Internet Res. 22(8), e22590 (2020). https://doi.org/10.2196/22590

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CoHASS, Nanyang Technological University, Singapore, Singapore

    Han Wei Tan

  2. WKWSCI, Nanyang Technological University, Singapore, Singapore

    Chei Sian Lee, Dion Hoe-Lian Goh, Han Zheng & Yin Leng Theng

Authors
  1. Han Wei Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chei Sian Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dion Hoe-Lian Goh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Han Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yin Leng Theng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chei Sian Lee .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tan, H.W., Lee, C.S., Goh, D.HL., Zheng, H., Theng, Y.L. (2021). Analyzing COVID-19 Vaccine Tweets for Tonal Shift. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021. Communications in Computer and Information Science, vol 1421. Springer, Cham. https://doi.org/10.1007/978-3-030-78645-8_78

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78645-8_78

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78644-1

  • Online ISBN: 978-3-030-78645-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation