Public Perception of COVID-19 Vaccines on Twitter in the United States

  1. Zidian Xie
  2. Xueting Wang
  3. Yan Jiang
  4. Yuhan Chen
  5. Shengyuan Huang
  6. Haoxuan Ma
  7. Ajay Anand
  8. Dongmei Li

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Abstract

Background

COVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines.

Objective

In this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as well as geographic and demographic characteristics of Twitter users who discussed about COVID-19 vaccines.

Methods

Through Twitter streaming Application Programming Interface (API), COVID-19-related tweets were collected from March 5 th , 2020 to January 25 th , 2021 using relevant keywords (such as “corona”, “covid19”, and “covid”). Based on geolocation information provided in tweets and vaccine-related keywords (such as “vaccine” and “vaccination”), we identified COVID-19 vaccine-related tweets from the US. Topic modeling and sentiment analysis were performed to examine public perception and discussion of COVID-19 vaccines. Demographic inference using computer vision algorithm (DeepFace) was performed to infer the demographic characteristics (age, gender and race/ethnicity) of Twitter users who tweeted about COVID-19 vaccines.

Results

Our longitudinal analysis showed that the discussion of COVID-19 vaccines on Twitter in the US reached a peak at the end of 2020. Average sentiment score for COVID-19 vaccine-related tweets remained relatively stable during our study period except for two big peaks, the positive peak corresponds to the optimism about the development of COVID-19 vaccines and the negative peak corresponds to worrying about the availability of COVID-19 vaccines. COVID-19 vaccine-related tweets from east coast states showed relatively high sentiment score. Twitter users from east, west and southern states of the US, as well as male users and users in age group 30-49 years, were more likely to discuss about COVID-19 vaccines on Twitter.

Conclusions

Public discussion and perception of COVID-19 vaccines on Twitter were influenced by the vaccine development and the pandemic, which varied depending on the geographics and demographics of Twitter users.

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  1. ScreenIT

    SciScore for 10.1101/2021.10.16.21265097: (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

    Experimental Models: Organisms/Strains
    SentencesResources
    We built the DeepFace model to estimate three demographic features, including race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Asian, Hispanic, and others), gender (male and female), and age groups (Age 18-24, Age 25-29, Age 30-49, Age 50-64, and Age 65+).
    Non-Hispanic White
    suggested: None
    Software and Algorithms
    SentencesResources
    We applied the vaderSentiment package in Python to calculate the sentiment score for each tweet, and calculate average sentiment scores for each week.
    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:
    There are some limitations in our study. Firstly, even though social media provides a rich data source, it does not provide the demographics of Twitter users. In this study, while we tried to estimate demographics (including age, gender, and race/ethnicity) of Twitter users, there might be some biases due to the accuracy of algorithm. Secondly, considering Twitter users only take up 20% of the US population, the unavailability of posts from private accounts, and many Twitter users did not provide valid geolocation information and profile pictures, our data cannot fully represent the general population. Furthermore, our study only focused on a specific time period during the COVID-19 pandemic, which need to be updated in the future.

    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.

    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.

  2. Version published to 10.1101/2021.10.16.21265097 on medRxiv