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Entropy-based detection of Twitter echo chambers.

Manuel Pratelli1,2, Fabio Saracco3,1,4, Marinella Petrocchi2,1

  • 1IMT School For Advanced Studies Lucca, Piazza San Francesco 19, Lucca 55100, Italy.

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Summary
This summary is machine-generated.

We developed an unbiased entropy-based method to detect echo chambers. Our study found limited echo chamber presence but significant impact on discourse and a link to misinformation in the Italian Covid-19 vaccination debate.

Keywords:
Twittercomplex networksecho chambersinformation theorymisinformation

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Area of Science:

  • Social Media Analysis
  • Computational Social Science
  • Information Science

Background:

  • Echo chambers, where users encounter information aligning with prior beliefs, are prevalent in online discussions.
  • Understanding their formation and impact is crucial for analyzing online discourse and information dissemination.

Purpose of the Study:

  • To introduce a novel, unbiased entropy-based method for detecting echo chambers.
  • To quantify the presence and impact of echo chambers in the Italian Twitter debate on Covid-19 vaccination.

Main Methods:

  • Development of a data-agnostic, entropy-based algorithm for echo chamber detection.
  • Application of the method to a dataset of the Italian Twitter debate concerning Covid-19 vaccination.

Main Results:

  • A low prevalence of users within echo chambers was observed (approximately 0.35%).
  • Despite low numbers, echo chamber users significantly influenced discourse, contributing nearly a third of all retweets.
  • Echo chambers in this context appeared to be a significant source of disinformation.

Conclusions:

  • The proposed entropy-based method offers an unbiased approach to identify echo chambers.
  • Even small echo chambers can disproportionately affect online discourse and potentially amplify misinformation, as seen in the Covid-19 vaccination debate.