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#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic

Ashwin Rao, Nazanin Sabri, Siyi Guo, Louiqa Raschid, Kristina Lerman

Abstract

Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its importance, the role of emotional and moral language in PHEs' communication during COVID-19 remains under explored. This study examines how PHEs and pseudo-experts communicated on Twitter during the pandemic, focusing on emotional and moral language and their engagement with political elites. Analyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to January 2021, alongside public responses, we identified key priorities and differences in messaging strategy. PHEs prioritize masking, healthcare, education, and vaccines, using positive emotional language like optimism. In contrast, pseudo-experts discuss therapeutics and lockdowns more frequently, employing negative emotions like pessimism and disgust. Negative emotional and moral language tends to drive engagement, but positive language from PHEs fosters positivity in public responses. PHEs exhibit liberal partisanship, expressing more positivity towards liberals and negativity towards conservative elites, while pseudo-experts show conservative partisanship. These findings shed light on the polarization of COVID-19 discourse and underscore the importance of strategic use of emotional and moral language by experts to mitigate polarization and enhance public trust.

#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic

Abstract

Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its importance, the role of emotional and moral language in PHEs' communication during COVID-19 remains under explored. This study examines how PHEs and pseudo-experts communicated on Twitter during the pandemic, focusing on emotional and moral language and their engagement with political elites. Analyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to January 2021, alongside public responses, we identified key priorities and differences in messaging strategy. PHEs prioritize masking, healthcare, education, and vaccines, using positive emotional language like optimism. In contrast, pseudo-experts discuss therapeutics and lockdowns more frequently, employing negative emotions like pessimism and disgust. Negative emotional and moral language tends to drive engagement, but positive language from PHEs fosters positivity in public responses. PHEs exhibit liberal partisanship, expressing more positivity towards liberals and negativity towards conservative elites, while pseudo-experts show conservative partisanship. These findings shed light on the polarization of COVID-19 discourse and underscore the importance of strategic use of emotional and moral language by experts to mitigate polarization and enhance public trust.
Paper Structure (23 sections, 5 equations, 16 figures, 4 tables)

This paper contains 23 sections, 5 equations, 16 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data and Methods
  4. Results
  5. Discussion
  6. Abbreviations
  7. Multimedia Appendix

Figures (16)

  • Figure 1: Retweet interactions. Nodes represent PHEs (green) and pseudo-experts (orange) and retweet interactions between them. Green edges represent interactions where a PHE was retweeted, and orange edges represent interactions where a pseudo-expert was retweeted. The size of the node is proportional to the number of times the expert was retweeted.
  • Figure 2: Hashtag usage. Wordclouds highlight the most prominent 100 hashtags used by PHEs and pseudo-experts on Twitter.
  • Figure 3: Comparing the activity of PHEs and pseudo-experts along each issue. Box plots compare the daily proportion of issue related tweets from PHEs and pseudo-experts. Mann-Whitney U Test with Bonferroni correction is used to assess significance. * indicates significance at $p<0.05$, ** - $p<0.01$, *** - $p<0.001$, **** - $p<0.0001$ and, ns - not-significant.
  • Figure 4: Timeline of attention to issues. Daily fraction of original tweets posted by (a) PHEs and (b) pseudo-experts related to each issue. We use 7-day rolling average to reduce noise. Major events are marked by vertical lines (A-G). (A) Lockdowns: March 15, 2020, stay-at-home orders start being issues across the mainland United States; (B) Healthcare: March 30, 2020; (C) Therapeutics: April 24, 2020 as President Trump proposes fighting off the virus with bleach; (D) Education: July 8, 2020, President Trump calls for schools to reopen; (E) Vaccines: November 9, 2020, Pfizer reports 93% efficacy in Phase-3 trials.
  • Figure 5: Comparing emotions expressed by PHEs and pseudo-experts in messages about the issues. Box plots compare the daily proportion of tweets from PHEs and pseudo-experts expressing various emotions. Mann-Whitney U Test with Bonferroni correction is used to assess significance. * indicates significance at $p<0.05$, ** - $p<0.01$, *** - $p<0.001$, **** - $p<0.0001$ and, ns - not-significant.
  • ...and 11 more figures