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Nonlinear spreading behavior across multi-platform social media universe.

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Harmful content spreads widely online when communities link frequently across platforms, even with low infection rates. Reducing link loss is key to preventing widespread outbreaks of misinformation and hate speech.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Mathematical Modeling

Background:

  • Online communities dynamically interconnect across multiple social media platforms.
  • Understanding the viral spread of harmful content (misinformation, hate speech) is a critical societal challenge.
  • Existing models often do not fully capture cross-platform community dynamics.

Purpose of the Study:

  • To develop a non-linear dynamical model for viral spreading of harmful content across interconnected online communities.
  • To identify analytic conditions for the onset of system-wide outbreaks.
  • To inform policy interventions against the spread of harmful online content.

Main Methods:

  • Development of a non-linear dynamical model.
  • Application of mean-field theory (Effective Medium Theory).
  • Comparison with detailed numerical simulations.

Main Results:

  • The model accurately predicts viral spreading dynamics.
  • An analytic condition for outbreak onset was derived.
  • System-wide spreading is predicted when inter-community linking rates are high and link loss rates are low, irrespective of infection vs. recovery rates.

Conclusions:

  • High rates of inter-community linking and low rates of link loss can lead to widespread harmful content outbreaks.
  • Effective Medium Theory provides a robust framework for analyzing cross-platform content dynamics.
  • Policymakers must consider multi-community dynamics when designing strategies to combat online misinformation and hate speech.