Analyzing COVID-19 Vaccine Tweets for Tonal Shift

Han Wei Tan; Chei Sian Lee; Dion Hoe Lian Goh; Han Zheng; Yin Leng Theng

doi:10.1007/978-3-030-78645-8_78

Analyzing COVID-19 Vaccine Tweets for Tonal Shift

Han Wei Tan, Chei Sian Lee^*, Dion Hoe Lian Goh, Han Zheng, Yin Leng Theng

^*Corresponding author for this work

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	HCI International 2021 - Posters - 23rd HCI International Conference, HCII 2021, Proceedings
Editors	Constantine Stephanidis, Margherita Antona, Stavroula Ntoa
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	615-623
Number of pages	9
ISBN (Print)	9783030786441
DOIs	https://doi.org/10.1007/978-3-030-78645-8_78
Publication status	Published - 2021
Externally published	Yes
Event	23rd International Conference on Human-Computer Interaction, HCII 2021 - Virtual, Online Duration: Jul 24 2021 → Jul 29 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1421
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	23rd International Conference on Human-Computer Interaction, HCII 2021
City	Virtual, Online
Period	7/24/21 → 7/29/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

General Computer Science
General Mathematics

Keywords

COVID-19
LIWC2015
Tonal analysis
Twitter
Vaccine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-78645-8_78

Cite this

Tan, H. W., Lee, C. S., Goh, D. H. L., Zheng, H., & Theng, Y. L. (2021). Analyzing COVID-19 Vaccine Tweets for Tonal Shift. In C. Stephanidis, M. Antona, & S. Ntoa (Eds.), HCI International 2021 - Posters - 23rd HCI International Conference, HCII 2021, Proceedings (pp. 615-623). (Communications in Computer and Information Science; Vol. 1421). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78645-8_78

Tan, Han Wei ; Lee, Chei Sian ; Goh, Dion Hoe Lian et al. / Analyzing COVID-19 Vaccine Tweets for Tonal Shift. HCI International 2021 - Posters - 23rd HCI International Conference, HCII 2021, Proceedings. editor / Constantine Stephanidis ; Margherita Antona ; Stavroula Ntoa. Springer Science and Business Media Deutschland GmbH, 2021. pp. 615-623 (Communications in Computer and Information Science).

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title = "Analyzing COVID-19 Vaccine Tweets for Tonal Shift",

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{\textquoteright}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{\textquoteright} 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.",

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note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 23rd International Conference on Human-Computer Interaction, HCII 2021 ; Conference date: 24-07-2021 Through 29-07-2021",

year = "2021",

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Tan, HW, Lee, CS , Goh, DHL, Zheng, H & Theng, YL 2021, Analyzing COVID-19 Vaccine Tweets for Tonal Shift. in C Stephanidis, M Antona & S Ntoa (eds), HCI International 2021 - Posters - 23rd HCI International Conference, HCII 2021, Proceedings. Communications in Computer and Information Science, vol. 1421, Springer Science and Business Media Deutschland GmbH, pp. 615-623, 23rd International Conference on Human-Computer Interaction, HCII 2021, Virtual, Online, 7/24/21. https://doi.org/10.1007/978-3-030-78645-8_78

Analyzing COVID-19 Vaccine Tweets for Tonal Shift. / Tan, Han Wei; Lee, Chei Sian ; Goh, Dion Hoe Lian et al.
HCI International 2021 - Posters - 23rd HCI International Conference, HCII 2021, Proceedings. ed. / Constantine Stephanidis; Margherita Antona; Stavroula Ntoa. Springer Science and Business Media Deutschland GmbH, 2021. p. 615-623 (Communications in Computer and Information Science; Vol. 1421).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Analyzing COVID-19 Vaccine Tweets for Tonal Shift

AU - Tan, Han Wei

AU - Lee, Chei Sian

AU - Goh, Dion Hoe Lian

AU - Zheng, Han

AU - Theng, Yin Leng

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

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KW - LIWC2015

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KW - Twitter

KW - Vaccine

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T3 - Communications in Computer and Information Science

SP - 615

EP - 623

BT - HCI International 2021 - Posters - 23rd HCI International Conference, HCII 2021, Proceedings

A2 - Stephanidis, Constantine

A2 - Antona, Margherita

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 23rd International Conference on Human-Computer Interaction, HCII 2021

Y2 - 24 July 2021 through 29 July 2021

ER -

Tan HW, Lee CS , Goh DHL, Zheng H, Theng YL. Analyzing COVID-19 Vaccine Tweets for Tonal Shift. In Stephanidis C, Antona M, Ntoa S, editors, HCI International 2021 - Posters - 23rd HCI International Conference, HCII 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 615-623. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-78645-8_78

Analyzing COVID-19 Vaccine Tweets for Tonal Shift

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

UN SDGs

Access to Document

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Cite this