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Area of Science:

  • Network Science
  • Computational Social Science
  • Information Science

Background:

  • The World Wide Web exhibits a bow-tie structure, characterized by core (SCC), entrance (IN), and exit (OUT) components.
  • Discursive communities on social media, particularly Twitter, are often analyzed as clusters of interacting accounts.
  • Understanding the structure and information flow within these online communities is crucial for analyzing information dissemination.

Purpose of the Study:

  • To investigate the structural organization of discursive communities on Twitter across various thematic datasets.
  • To determine if these communities exhibit bow-tie structures, similar to the World Wide Web.
  • To analyze the quality of content within different sectors of these structures, especially in relation to misinformation and the infodemic phenomenon.

Main Methods:

  • Analysis of 8 diverse thematic Twitter datasets in multiple languages.
  • Identification and characterization of discursive communities based on retweet interactions.
  • Application of the bow-tie model to describe community structure (IN, OUT, SCC).
  • Content quality assessment using domain annotations from NewsGuard, a fact-checking resource.

Main Results:

  • A significant majority of discursive communities studied displayed a bow-tie structure during political and societal debates.
  • This bow-tie structure was notably absent in discussions on non-political topics like sports events.
  • Content quality analysis revealed that the Strongly Connected Component (SCC) and the flow between SCC and OUT sectors contained the most low-reputation and non-reputable messages, particularly in misinformation-affected communities.

Conclusions:

  • Discursive communities on Twitter can adopt bow-tie structures, mirroring the World Wide Web, especially during contentious debates.
  • The prevalence of low-quality information within the core and connecting sectors of these structures contributes to the infodemic.
  • The findings highlight the structural vulnerabilities in online discussions that facilitate the spread of misinformation and overwhelm users with excessive, low-quality information.