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Social contagion with degree-dependent thresholds.

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Summary
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This study explores how individual influence and opinion thresholds affect opinion spreading. Strong positive coupling leads to diverse opinions, while weaker coupling results in consensus.

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Area of Science:

  • Social network analysis
  • Computational social science
  • Complex systems modeling

Background:

  • Understanding opinion dynamics is crucial in social networks.
  • Heterogeneous influence distribution significantly impacts information diffusion.
  • Threshold models are widely used to simulate adoption processes.

Purpose of the Study:

  • To investigate opinion spreading using a threshold model with heterogeneous influence.
  • To analyze the coupling between individual influence (out-degree) and opinion adoption thresholds.
  • To determine how this coupling affects the final state of opinion distribution.

Main Methods:

  • Utilized a threshold model for opinion spreading simulations.
  • Incorporated heterogeneous distribution of individual influence (out-degree).
  • Examined the relationship between influence and the threshold for adopting new opinions.

Main Results:

  • A strongly positive coupling between influence and threshold leads to a mix of opinions.
  • Weaker or non-positive coupling results in convergence to a consensus state.
  • The observed phenomena are a complex interplay of mechanisms, not a simple phase transition.

Conclusions:

  • The relationship between individual influence and opinion thresholds is a key determinant of opinion dynamics.
  • System behavior (consensus vs. diversity) depends on the strength and nature of this coupling.
  • Future research should consider the nuanced interplay of factors in opinion spreading models.