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The Shifting Landscape of Vaccine Discourse: Insights From a Decade of Pre- to Post-COVID-19 Vaccine Posts on Social Media

Nikesh Gyawali, Doina Caragea, Cornelia Caragea, Saif M. Mohammad

TL;DR

It is shown that the COVID-19 pandemic led to complex shifts in X users’ sentiment and discourse around vaccines, and it was observed that negative emotion word usage decreased during the pandemic, with notable rises in usage of surprise, and trust related emotion words.

Abstract

In this work, we study English-language vaccine discourse in social media posts, specifically posts on X (formerly Twitter), in seven years before the COVID-19 outbreak (2013 to 2019) and three years after the outbreak was first reported (2020 to 2022). Drawing on theories from social cognition and the stereotype content model in Social Psychology, we analyze how English speakers talk about vaccines on social media to understand the evolving narrative around vaccines in social media posts. To do that, we first introduce a novel dataset comprising 18.7 million curated posts on vaccine discourse from 2013 to 2022. This extensive collection-filtered down from an initial 129 million posts through rigorous preprocessing-captures both pre-COVID and COVID-19 periods, offering valuable insights into the evolution of English-speaking X users' perceptions related to vaccines. Our analysis shows that the COVID-19 pandemic led to complex shifts in X users' sentiment and discourse around vaccines. We observe that negative emotion word usage decreased during the pandemic, with notable rises in usage of surprise, and trust related emotion words. Furthermore, vaccine-related language tended to use more warmth-focused words associated with trustworthiness, along with positive, competence-focused words during the early days of the pandemic, with a marked rise in negative word usage towards the end of the pandemic, possibly reflecting a growing vaccine hesitancy and skepticism.

The Shifting Landscape of Vaccine Discourse: Insights From a Decade of Pre- to Post-COVID-19 Vaccine Posts on Social Media

TL;DR

It is shown that the COVID-19 pandemic led to complex shifts in X users’ sentiment and discourse around vaccines, and it was observed that negative emotion word usage decreased during the pandemic, with notable rises in usage of surprise, and trust related emotion words.

Abstract

In this work, we study English-language vaccine discourse in social media posts, specifically posts on X (formerly Twitter), in seven years before the COVID-19 outbreak (2013 to 2019) and three years after the outbreak was first reported (2020 to 2022). Drawing on theories from social cognition and the stereotype content model in Social Psychology, we analyze how English speakers talk about vaccines on social media to understand the evolving narrative around vaccines in social media posts. To do that, we first introduce a novel dataset comprising 18.7 million curated posts on vaccine discourse from 2013 to 2022. This extensive collection-filtered down from an initial 129 million posts through rigorous preprocessing-captures both pre-COVID and COVID-19 periods, offering valuable insights into the evolution of English-speaking X users' perceptions related to vaccines. Our analysis shows that the COVID-19 pandemic led to complex shifts in X users' sentiment and discourse around vaccines. We observe that negative emotion word usage decreased during the pandemic, with notable rises in usage of surprise, and trust related emotion words. Furthermore, vaccine-related language tended to use more warmth-focused words associated with trustworthiness, along with positive, competence-focused words during the early days of the pandemic, with a marked rise in negative word usage towards the end of the pandemic, possibly reflecting a growing vaccine hesitancy and skepticism.

Paper Structure

This paper contains 17 sections, 3 equations, 7 figures, 4 tables.

Table of Contents

  1. Abstract
  2. Introduction
  3. Related works
  4. Data collection
  5. Methodology
  6. Results and discussion
  7. Conclusions
  8. Limitations and future research
  9. Supporting information

Figures (7)

  • Figure 1: Temporal trends in vaccine-related posts and platform engagement activities from 2013-2022. (A) Monthly volume of vaccine‐related posts is shown in $log_{10}$ scale. The orange line represents the total number of vaccine‐related tweets posted ("actual posts"), and the blue line represents the tweets crawled by full-archive search ("crawled posts"). (B) Platform-level engagement metrics are plotted for the same period. The green line shows Twitter's Monthly Active Users (MAU), and the dotted red line represents monetizable Daily Active Users (mDAU), based on SEC filings prior to privatization. Twitter shifted from reporting MAU to mDAU in April 2019.
  • Figure 2: Distribution of word count and character count per post. The x-axis shows the count of words or characters, and the y-axis shows the frequency of posts in units of 100,000.
  • Figure 3: Monthly trend in emotion-word density of positive and negative sentiment words from 2013 to 2022. Darker lines show the three-month rolling averages of emotion-word density, and lighter lines represent the observed monthly values. Emotion-word density is calculated as the total number of emotion-related words used in a month divided by the total number of words posted in that month.
  • Figure 4: Monthly trends in emotion-word density during the COVID-19 pandemic (2020--2022). Emotion-word density is calculated as the total number of emotion-related words used in a month divided by the total number of words posted in that month.
  • Figure 5: Comparison of vaccine-discourse "home bases" before and during COVID-19. The red ellipse represents home bases for pre-COVID-19 years and the blue during the COVID-19 years in the warmth–competence space.
  • ...and 2 more figures