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Updated: Aug 12, 2025
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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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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.
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.
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