Opinion dynamics with backfire effect and biased assimilation

Area of Science:

• Social Sciences
• Computational Social Science
• Network Science

Background:

• The proliferation of AI content generation and mass media access blurs the lines between fact and fiction.
• Understanding opinion evolution in networked environments lacking objective reality is a significant challenge.
• Existing models often simplify social influence, neglecting key psychological phenomena.

Purpose of the Study:

• To propose a novel opinion formation model integrating Biased Assimilation and the Backfire Effect.
• To analyze opinion dynamics within social networks under these combined psychological influences.
• To identify conditions leading to opinion polarization and consensus.

Main Methods:

• Development of a new DeGroot-type opinion formation model.
• Incorporation of Biased Assimilation: adopting similar opinions.
• Inclusion of the Backfire Effect: entrenchment in opposing views.
• Theoretical and empirical analysis of the model's behavior.

Main Results:

• The model captures the Backfire Effect in opinion dynamics, a novel contribution.
• Identified intuitive conditions governing the emergence of polarization.
• Revealed conditions conducive to reaching consensus within the network.
• Characterized the properties of opinions resulting from the model's dynamics.

Conclusions:

• The proposed model offers a more realistic framework for opinion formation in complex social networks.
• Biased Assimilation and the Backfire Effect significantly influence opinion polarization and consensus.
• The findings provide insights into how individuals' opinions evolve amidst misinformation and social influence.