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Information Pathways in Online Science Communication: The Role of Platform Actors and News Media

Alexandros Efstratiou, Giuseppe Russo, Luca Luceri

Abstract

Online discussions of science involve complex interactions among experts, news media, and social media users as they interpret and disseminate scientific findings. While prior work has examined these actors in isolation, their interplay in shaping science communication remains poorly understood. Using the COVID-19 pandemic as a case study, we analyze 1.24M tweets and 211k news articles that reference pandemic-related scientific papers. We find that the most influential Twitter accounts in this discourse are predominantly individuals with medical or research credentials. However, we also identify a coordinated network that disproportionately amplifies a small set of prominent credentialed experts who advance contrarian, anti-consensus positions on vaccines, lockdowns, and related topics. The papers promoted by these influential actors substantially overlap with those covered by news media, but with key differences: pro-consensus experts primarily engage with studies featured by mainstream and medical outlets, whereas contrarian experts align more closely with papers promoted by low-quality, pseudoscientific, or conspiratorial sources. Notably, news outlets tend to report on scientific studies after they have been highlighted by social media superspreaders. Together, these findings reveal multi-level pathways of information flow and coordinated amplification structures that shape science communication across social media and news, offering new insights into the dynamics of the broader information ecosystem.

Information Pathways in Online Science Communication: The Role of Platform Actors and News Media

Abstract

Online discussions of science involve complex interactions among experts, news media, and social media users as they interpret and disseminate scientific findings. While prior work has examined these actors in isolation, their interplay in shaping science communication remains poorly understood. Using the COVID-19 pandemic as a case study, we analyze 1.24M tweets and 211k news articles that reference pandemic-related scientific papers. We find that the most influential Twitter accounts in this discourse are predominantly individuals with medical or research credentials. However, we also identify a coordinated network that disproportionately amplifies a small set of prominent credentialed experts who advance contrarian, anti-consensus positions on vaccines, lockdowns, and related topics. The papers promoted by these influential actors substantially overlap with those covered by news media, but with key differences: pro-consensus experts primarily engage with studies featured by mainstream and medical outlets, whereas contrarian experts align more closely with papers promoted by low-quality, pseudoscientific, or conspiratorial sources. Notably, news outlets tend to report on scientific studies after they have been highlighted by social media superspreaders. Together, these findings reveal multi-level pathways of information flow and coordinated amplification structures that shape science communication across social media and news, offering new insights into the dynamics of the broader information ecosystem.
Paper Structure (36 sections, 15 figures, 6 tables)

This paper contains 36 sections, 15 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Research questions.
  3. Methods.
  4. Main findings.
  5. Related Work
  6. Science (Mis)communication
  7. Social Collaboration and Coordination
  8. Present Work
  9. Dataset
  10. Characterizing On-Platform Actors
  11. Detection of Coordinated Retweet Networks
  12. Profiles.
  13. Activity.
  14. Superspreaders
  15. Profiles.
  16. ...and 21 more sections

Figures (15)

  • Figure 1: Network of coordinated accounts based on co-retweet activity.
  • Figure 2: Violin plots for distribution comparisons between coordinated and non-coordinated accounts.
  • Figure 3: Violin plots for distribution comparisons between superspreader and non-superspreader characteristics.
  • Figure 4: Word clouds for main keywords and hashtags by superspreader type.
  • Figure 5: Distribution of most-retweeted accounts by superspreader and related stance.
  • ...and 10 more figures