The Spread of Pro- and Anti-Vaccine Views by Coordinated Communities on Facebook during COVID-19 Pandemic

Yunya Song, Yin Zhang, Sheng Zou, Xian Yang*, Qintao Huang, Zhijun Song

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

The widespread dissemination of problematic vaccine-related content during the COVID-19 pandemic has posed serious challenges to public health and eroded institutional trust. This study investigates the interplay between manipulative social media actors, coordinated behaviors, and misleading information by analyzing coordinated link sharing behavior (CLSB) on Facebook in the United Kingdom and the United States. Drawing on a dataset of 3,469,719 public Facebook posts, we examine whether anti-vaccine content was disseminated more systematically and inauthentically than pro-vaccine content. We also trace the evolution of coordinated narratives over time and their cross-national variations. Methodologically, we apply computational techniques, including transfer learning for sentiment classification and structural topic modeling, to detect pro- and anti-vaccine stances and to identify thematic patterns within coordinated networks. Our findings reveal that in the UK, anti-vaccine entities exhibited denser CLSB networks and greater engagement than their pro-vaccine counterparts, whereas the opposite trend was observed in the U.S. Furthermore, we identify key differences in vaccine discourse: UK anti-vaccine communities predominantly emphasized vaccine safety concerns, while U.S. communities focused more on individual freedom. These cross-national comparisons highlight how political and cultural contexts shape the structure and rhetoric of vaccine-related coordination online.
Original languageEnglish
JournalJournal of Computational Social Science
Publication statusAccepted/In press - 18 Jun 2025

User-Defined Keywords

  • COVID-19
  • social network analysis
  • Facebook
  • vaccine attitude
  • coordinated link sharing behavior
  • problematic information

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