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Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
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Steering polarization toward consensus in signed majority-vote opinion models.

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A novel bias in signed social networks can dissolve polarization and promote consensus. This structure-aware intervention is effective even in large networks with many opposing ties.

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Area of Science:

  • Social network analysis
  • Computational social science
  • Opinion dynamics

Background:

  • Signed social networks, representing both positive and negative relationships, often lead to persistent opinion polarization.
  • Understanding and mitigating this polarization is crucial for social cohesion.

Purpose of the Study:

  • To develop and evaluate a method for dissolving polarization in signed social networks.
  • To investigate the effectiveness of a dynamic, sign-matched edge bias in promoting consensus.

Main Methods:

  • Modification of the fast gradient-sign method to include a uniform, sign-matched edge bias.
  • Experiments conducted on synthetic graphs and large real-world datasets.

Main Results:

  • A moderate level of the proposed bias effectively dissolves polarization and fosters broad consensus.
  • Strength-matched random perturbations, in contrast, preserve opinion divisions.
  • The required bias increases with the density of antagonistic ties but is relatively insensitive to network size.

Conclusions:

  • Lightweight, structure-aware interventions can restore consensus in large signed networks.
  • This approach offers a feasible strategy for mitigating online social division and polarization.