PACIFIER is a graph RL framework that matches analytical solvers for minimizing polarization and outperforms baselines across multiple intervention regimes on real Twitter networks up to 155k nodes by training on small synthetic graphs.
Opinion dynamics and bounded confidence models, analysis, and simulation.Journal of Artificial Societies and Social Simulation, 5(3):2
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Empirical analysis of three Reddit and Twitter datasets shows opinion homophily in interaction networks exceeds chance levels, amplified by tie strength and topic polarization, with asymmetric tolerance favoring mainstream positions, supporting a bounded confidence model of value homophily.
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PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework
PACIFIER is a graph RL framework that matches analytical solvers for minimizing polarization and outperforms baselines across multiple intervention regimes on real Twitter networks up to 155k nodes by training on small synthetic graphs.
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Quantifying opinion homophily in online social networks: A bounded confidence perspective
Empirical analysis of three Reddit and Twitter datasets shows opinion homophily in interaction networks exceeds chance levels, amplified by tie strength and topic polarization, with asymmetric tolerance favoring mainstream positions, supporting a bounded confidence model of value homophily.