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Mitigating opinion polarization in social networks using adversarial attacks.

Michinori Ninomiya1, Genki Ichinose2, Katsumi Chiyomaru3

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Summary

This study shows that strategically adding perturbations to social network links can mitigate opinion polarization. This method is more effective in larger networks, offering a way to prevent division in online communities.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Opinion Dynamics

Background:

  • Social networking services (SNS) facilitate connections among like-minded individuals.
  • This can lead to decreased exposure to diverse viewpoints and increased opinion polarization.
  • Existing research has explored conditions for polarization but lacked mitigation strategies.

Purpose of the Study:

  • To investigate the potential of artificial perturbations to mitigate opinion polarization in network models.
  • To determine if adversarial-inspired perturbations can reverse or reduce opinion divergence.

Main Methods:

  • Conducted numerical simulations of opinion dynamics models.
  • Introduced strategic perturbations to the weights of network links.
  • Analyzed the impact of perturbation strength and network size on polarization.

Main Results:

  • Opinion polarization can be effectively mitigated by strategically perturbing network link weights.
  • The degree of mitigation increases with the strength of the applied perturbations.
  • The proposed method for mitigating polarization is more effective in larger network structures.

Conclusions:

  • Strategic perturbation of network links offers a viable method to counteract opinion polarization.
  • This approach shows promise for fostering more diverse opinion landscapes in online social networks.
  • The effectiveness scales with network size, suggesting applicability to large-scale platforms.