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Related Experiment Video

Updated: Aug 12, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Understanding dynamics of polarization via multiagent social simulation.

Amanul Haque¹, Nirav Ajmeri², Munindar P Singh¹

¹Department of Computer Science, North Carolina State University, Raleigh, NC USA.

AI & Society

|January 30, 2023

Summary

Higher user tolerance in social networks slows polarization but decreases satisfaction. Selective exposure increases polarization and reduces user reach, fostering homophily.

Keywords:

Echo chambers Selective exposure Social networks User tolerance

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Area of Science:

Social Sciences
Computer Science
Public Health

Background:

The internet, particularly social networks, is recognized for driving user polarization across various domains, including politics and public health (e.g., vaccination attitudes).
Understanding polarization dynamics in online social networks is complex, requiring consideration of user attitudes, interactions, and information exposure.

Purpose of the Study:

To analyze how content sharing influences user satisfaction and polarization within social networks.
To investigate the impact of varying user tolerance levels and selective exposure to congenial viewpoints on polarization.

Main Methods:

Utilized Social Judgment Theory to operationalize attitude shifts and model user behavior based on existing empirical data.
Designed and implemented a social simulation to study the effects of content sharing, user tolerance, and selective exposure.

Main Results:

Increased user tolerance was found to decelerate polarization but resulted in diminished user satisfaction.
Higher levels of selective exposure correlated with increased polarization and reduced user reach.
Both elevated tolerance and greater selective exposure contributed to the formation of more homophilic social networks.

Conclusions:

User tolerance and selective exposure are critical factors influencing polarization and network structure in social media.
Simulation results highlight the trade-offs between polarization, user satisfaction, and network reach based on user behavior and network parameters.