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<p>This paper examines political communication on Brazilian X/Twitter during the early months of the COVID-19 pandemic, a period marked by tensions between federal authorities, state governors, and media actors. We consider how the platform reflects the circulation of policy-related narratives, including controversial positions such as early treatment with hydroxychloroquine. To support the analysis of large-scale interaction data, we construct a retweet network (March–June 2020) and apply a graph-embedding mapping approach combined with topological clustering. This method is designed to enable the visual exploration of very large networks, which are otherwise computationally demanding to analyze and interpret. By organizing the network into regions of influence, the approach provides a structured view of interaction patterns across different groups of actors. The results illustrate how media and hyperpartisan content occupy distinct regions of the network and how interaction patterns evolve around key political actors during the period.</p>

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Mapping COVID-19 information and political dynamics on X/Twitter through graph-embedding cartography of large retweet networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Thiago Ciodaro,
  • Vitor do Carmo,
  • Fernando Ferreira,
  • Miguel Lago,
  • Debora Salles,
  • Marie Santini

摘要

This paper examines political communication on Brazilian X/Twitter during the early months of the COVID-19 pandemic, a period marked by tensions between federal authorities, state governors, and media actors. We consider how the platform reflects the circulation of policy-related narratives, including controversial positions such as early treatment with hydroxychloroquine. To support the analysis of large-scale interaction data, we construct a retweet network (March–June 2020) and apply a graph-embedding mapping approach combined with topological clustering. This method is designed to enable the visual exploration of very large networks, which are otherwise computationally demanding to analyze and interpret. By organizing the network into regions of influence, the approach provides a structured view of interaction patterns across different groups of actors. The results illustrate how media and hyperpartisan content occupy distinct regions of the network and how interaction patterns evolve around key political actors during the period.