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Area of Science:

  • Computational Social Science
  • Network Science
  • Mathematical Modeling

Background:

  • Online social networks are central to communication but foster undesirable effects like hostility and polarization.
  • Analytical tools are needed to understand the dynamics of these complex social systems.
  • Discord, hostility, and echo chambers are key phenomena requiring quantitative study.

Purpose of the Study:

  • To introduce a novel method for calculating the probability of discord between agents in social networks.
  • To analyze the evolution of discord in the multistate voter model, with and without zealots.
  • To provide a generalizable framework applicable to diverse network structures and opinion dynamics.

Main Methods:

  • Formal introduction of a method to compute discord probability in multistate voter models.
  • Application to any directed, weighted graph with finite opinions and varied agent update rates.
  • Development of a linear system of ordinary differential equations to describe discord evolution.
  • Proof of a unique equilibrium solution computable via an iterative algorithm.

Main Results:

  • A generalized definition of active links density accounting for long-range, weighted interactions.
  • Demonstration of findings on real-life and synthetic networks.
  • Investigation into the impact of network clustering on discord.
  • Uncovering varied behaviors in polarized networks and between antagonistic communities.

Conclusions:

  • The proposed method offers a precise, non-approximated way to quantify discord in social networks.
  • Understanding discord evolution is crucial for mitigating negative online social phenomena.
  • Network topology, particularly clustering, significantly influences discord dynamics in polarized environments.