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. 2022 Sep 2;10(9):1457.
doi: 10.3390/vaccines10091457.

Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period

Qin Xiang Ng  1 , Shu Rong Lim  1 , Chun En Yau  2 , Tau Ming Liew  3   4   5
Affiliations

Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period

Qin Xiang Ng et al. Vaccines (Basel). .

Abstract

Despite the demonstrated efficacy, safety, and availability of COVID-19 vaccines, efforts in global mass vaccination have been met with widespread scepticism and vaccine hesitancy or refusal. Understanding the reasons for the public's negative opinions towards COVID-19 vaccination using Twitter may help make new headways in improving vaccine uptake. This study, therefore, examined the prevailing negative sentiments towards COVID-19 vaccination via the analysis of public twitter posts over a 16 month period. Original tweets (in English) from 1 April 2021 to 1 August 2022 were extracted. A bidirectional encoder representations from transformers (BERT)-based model was applied, and only negative sentiments tweets were selected. Topic modelling was used, followed by manual thematic analysis performed iteratively by the study investigators, with independent reviews of the topic labels and themes. A total of 4,448,314 tweets were analysed. The analysis generated six topics and three themes related to the prevailing negative sentiments towards COVID-19 vaccination. The themes could be broadly understood as either emotional reactions to perceived invidious policies or safety and effectiveness concerns related to the COVID-19 vaccines. The themes uncovered in the present infodemiology study fit well into the increasing vaccination model, and they highlight important public conversations to be had and potential avenues for future policy intervention and campaign efforts.

Keywords: COVID-19; machine learning; negative sentiment; sentiment analysis; vaccines.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Unsupervised machine learning of free-text data from Twitter using BERT.
Figure 2
Figure 2
Flowchart illustrating tweet selection process.
Figure 3
Figure 3
Temporal variations in the normalised frequency of tweets belonging to Theme 1 (Topics 1 and 2), Theme 2 (Topics 3 and 4), and Theme 3 (Topics 5 and 6).
Figure 4
Figure 4
An illustration of how the themes fit into the increasing vaccination model [17].
See this image and copyright information in PMC

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References

    1. Lin D.Y., Gu Y., Wheeler B., Young H., Holloway S., Sunny S.K., Moore Z., Zeng D. Effectiveness of COVID-19 Vaccines over a 9-Month Period in North Carolina. N. Engl. J. Med. 2022;386:933–941. doi: 10.1056/NEJMoa2117128. - DOI - PMC - PubMed
    1. Liew T.M., Lee C.S. Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts. JMIR Public Health Surveill. 2021;7:e29789. doi: 10.2196/29789. - DOI - PMC - PubMed
    1. Yousefinaghani S., Dara R., Mubareka S., Papadopoulos A., Sharif S. An analysis of COVID-19 vaccine sentiments and opinions on Twitter. Int. J. Infect. Dis. 2021;108:256–262. doi: 10.1016/j.ijid.2021.05.059. - DOI - PMC - PubMed
    1. Balakrishnan V., Zhen N.W., Chong S.M., Han G.J., Lee T.J. Infodemic and fake news—A comprehensive overview of its global magnitude during the COVID-19 pandemic in 2021: A scoping review. Int. J. Disaster Risk Reduct. 2022;1:103144. doi: 10.1016/j.ijdrr.2022.103144. - DOI - PMC - PubMed
    1. 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. Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019. Vaccines. 2021;9:28. doi: 10.3390/vaccines9010028. - DOI - PMC - PubMed

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