Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

  1. Tao Hu
  2. Siqin Wang
  3. Wei Luo
  4. Mengxi Zhang
  5. Xiao Huang
  6. Yingwei Yan
  7. Regina Liu
  8. Kelly Ly
  9. Viraj Kacker
  10. Bing She
  11. Zhenlong Li

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance.

Objective

The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter.

Methods

We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes.

Results

An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis.

Conclusions

The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

Article activity feed

  1. Version published to 10.2196/30854
  2. ScreenIT

    SciScore for 10.1101/2021.06.02.21258233: (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
    First, we generalized the variations of COVID-related terms to “COVID-19”, including “corona”, “covid”, “covid19” and “coronavirus”; second, we removed the unrelated website links from the searching results, including the links starting from the fragment of “https”; third, we removed the punctuations (e.g., a period, question mark, comma, colon, and ellipsis) and other key symbols (e.g., a bracket, single and double quote) and converted the capital into lower case; fourth, we removed the inflectional endings (e.g., “ly”) and returned the root or dictionary form of a word by employing the function of word lemmatization provided in the Python package Natural Language Toolkit 3.6.2 [24].
    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:
    Limitation and future work: Our study has several limitations that can be improved in future studies. First, the demographic of Twitter users is typically characterized by younger users who are avid users of mobile phone apps and the Internet, and such users may not be able to reflect the opinion and perception of the general public with varying demographics and socioeconomic status [49]. In addition, the representativeness of Twitter users is not stationary but geographically varying. Like other studies that rely on digital devices, the “Digital Divide” [50] issue needs to be acknowledged. This study only accounts for the reactions from Twitter users to vaccines, which neglect the underprivileged members of society to some degree, especially the poor, elderly, and those living in rural areas that do not have access to digital devices, as well as those who are not willing to share their thoughts on social media platforms. Additionally, the Twitter API we used allows limited access to approximately 1% of the total records. Future work needs to increase the sample size to reduce the uncertainties and fluctuations of sentiment scores and emotions. In early 2021, Twitter released a new Twitter API (academic research product track) that grants free access to full-archive search with enhanced features and functionality for researchers to obtain more precise, complete, and unbiased data for analyzing the public conversation [51]. Further efforts can be made to explore the potential ...

    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.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.

    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.

  3. Version published to 10.1101/2021.06.02.21258233v1 on medRxiv
  4. Version published to 10.2196/preprints.30854