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Reversible bootstrap percolation: Fake news and fact checking.

Matías A Di Muro1, Sergey V Buldyrev2, Lidia A Braunstein3

  • 1Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR), Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata-CONICET, Funes 3350, (7600) Mar del Plata, Argentina.

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Summary

This study introduces a reversible bootstrap percolation model to explain fake news spread and fact-checking effectiveness. It reveals that removing fake news sources may not always reverse established public belief.

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Area of Science:

  • Complex Systems
  • Social Dynamics
  • Network Science

Background:

  • Bootstrap percolation models social phenomena like opinion formation.
  • Standard models can yield multiple stable states, indicating complex dynamics.
  • Existing models lack mechanisms to explain hysteresis and the persistence of beliefs.

Purpose of the Study:

  • To develop a reversible bootstrap percolation process.
  • To model the spread of fake news and the efficacy of fact-checking.
  • To investigate phenomena like hysteresis in opinion dynamics.

Main Methods:

  • Construction of a reversible bootstrap percolation process.
  • Analysis of convergence to multiple stable fixed points.
  • Simulation of fake news dissemination and fact-checking interventions.

Main Results:

  • The reversible process exhibits hysteresis, characteristic of discontinuous phase transitions.
  • Demonstrates that eliminating fake news sources may be insufficient to alter entrenched beliefs.
  • Identifies conditions under which belief reversal is challenging.

Conclusions:

  • The reversible bootstrap percolation model offers insights into fake news dynamics.
  • Hysteresis in belief formation highlights the difficulty in reversing misinformation.
  • Fact-checking strategies need to consider the persistence of established opinions.