Opinion cascade under perception bias in social networks

Area of Science:

• Social network analysis
• Opinion dynamics
• Computational social science

Background:

• Opinion cascades are crucial for understanding consensus and disagreement.
• The influence of biased perceptions on opinion cascades is understudied.
• Existing models often overlook the interplay between global and local information.

Purpose of the Study:

• To investigate the impact of perception bias on opinion cascades in social networks.
• To introduce and analyze a threshold model for opinion dynamics.
• To identify conditions leading to consensus versus disagreement.

Main Methods:

• Simulation of opinion cascade processes using a threshold model.
• Analysis of social networks with varying degrees of perception bias and stubbornness.
• Examination of phase transitions between consensus and disagreement.

Main Results:

• Consensus is achieved only when collective stubbornness is below a critical threshold.
• Decreasing stubbornness increases the occurrence of first- and second-order phase transitions.
• Disagreement arises from echo chambers where biased perceptions prevent opinion erosion.

Conclusions:

• Perception bias significantly influences opinion formation and cascade dynamics.
• Echo chambers contribute to the persistence of disagreement.
• The study provides a framework for understanding and potentially mitigating disagreement in biased information environments.