Who Says What in Which Networks: What influences Social Media Users’ Emotional Reactions to the COVID-19 Vaccine Infodemic?

Aimei Yang, Shin Jieun, Hye Min Kim, Alvin Zhou, Wenlin Liu, Ke Huang-Isherwood, Eugene Jang, Jingyi Sun, Eugene Lee, Zhang Yafei, Dong Chuqin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This study aims to identify effective predictors that influence publics’ emotional reactions to COVID-19 vaccine misinformation as well as corrective messages. We collected a large sample of COVID-19 vaccine related misinformation and corrective messages on Facebook as well as the users’ emotional reactions (i.e., emojis) to these messages. Focusing on three clusters of features such as messages’ linguistic features, source characteristics, and messages’ network positions, we examined whether users’ reactions to misinformation and corrective information would differ. We used random forest models to identify the most salient predictors among over 70 predictors for both types of messages. Our analysis found that for misinformation, political ideology of the message source was the most salient feature that predicted anxious and enthusiastic reactions, followed by message features that highlight personal concerns and messages’ network positions. For corrective messages, while the sources’ ideology was still key to raising anxiety, the most important feature for triggering enthusiasm was the messages’ network positions and message quality.

Original languageEnglish (US)
Pages (from-to)1986-2009
Number of pages24
JournalSocial Science Computer Review
Volume41
Issue number6
DOIs
StatePublished - Dec 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2022.

Keywords

  • COVID-19
  • corrective messages
  • machine learning
  • misinformation
  • natural language processing
  • social network analysis

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