Public Reactions towards the COVID-19 Pandemic on Twitter in the United Kingdom and the United States

  1. Canruo Zou
  2. Xueting Wang
  3. Zidian Xie
  4. Dongmei Li

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Abstract

Background

The coronavirus disease 2019 (COVID-19) has spread globally since December 2019. Twitter is a popular social media platform with active discussions about the COVID-19 pandemic. The public reactions on Twitter about the COVID-19 pandemic in different countries have not been studied. This study aims to compare the public reactions towards the COVID-19 pandemic between the United Kingdom and the United States from March 6, 2020 to April 2, 2020.

Data

The numbers of confirmed COVID-19 cases in the United Kingdom and the United States were obtained from the 1Point3Acres website. Twitter data were collected using COVID-19 related keywords from March 6, 2020 to April 2, 2020.

Methods

Temporal analyses were performed on COVID-19 related Twitter posts (tweets) during the study period to show daily trends and hourly trends. The sentiment scores of the tweets on COVID-19 were analyzed and associated with the policy announcements and the number of confirmed COVID-19 cases. Topic modeling was conducted to identify related topics discussed with COVID-19 in the United Kingdom and the United States.

Results

The number of daily new confirmed COVID-19 cases in the United Kingdom was significantly lower than that in the United States during our study period. There were 3,556,442 COVID-19 tweets in the United Kingdom and 16,280,065 tweets in the United States during the study period. The number of COVID-19 tweets per 10,000 Twitter users in the United Kingdom was lower than that in the United States. The sentiment scores of COVID-19 tweets in the United Kingdom were less negative than those in the United States. The topics discussed in COVID-19 tweets in the United Kingdom were mostly about the gratitude to government and health workers, while the topics in the United States were mostly about the global COVID-19 pandemic situation.

Conclusion

Our study showed correlations between the public reactions towards the COVID-19 pandemic on Twitter and the confirmed COVID-19 cases as well as the policies related to the COVID-19 pandemic in the United Kingdom and the United States.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.25.20162024: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data preprocessing: Using a Python script, the tweets without COVID-19 related keywords in the text were filtered out.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. ScreenIT

    SciScore for 10.1101/2020.07.25.20162024: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data preprocessing Using a Python script, the tweets without COVID-19 related keywords in the text were filtered out.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

    Limitations There were several limitations in our study. First, although Twitter is one of the most popular social media platforms [27], the Twitter users may not represent the whole population. Second, our study only focused on Twitter data from March 6 to April 2, 2020. COVID-19 is an ongoing pandemic and public reactions towards COVID-19 might evolve after April 2, 2020. Third, the geographic information used in our study might have some biases as the user geolocation in their profile could be inaccurate. Conclusion By analyzing COVID-19 related tweets from March 6 to April 2, 2020 in the United Kingdom and the United States, we showed the differences in the public attitudes towards COVID-19 in different countries in a timely manner, which might correlate with the number of COVID-19 cases and some important policies/news related to COVID-19. Our study provides some evidence about the correlation between the severity of COVID-19 pandemic and the public attitudes towards COVID-19, especially how different policies from different countries affect the public attitudes towards the COVID-19 pandemic.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. ScreenIT

    SciScore for 10.1101/2020.07.25.20162024: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data preprocessing Using a Python script, the tweets without COVID-19 related keywords in the text were filtered out.
    Python
    suggested: (IPython, SCR_001658)

    Data from additional tools added to each annotation on a weekly basis.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.07.25.20162024v1 on medRxiv