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This research aims to explain opinion polarization and clustering phenomena in social networks through a control-theoretic lens.

July 11, 2026Future InternetOpen Access

Opinion Dynamics in Social Networks with Edge-Heterogeneous Confidence Bounds: Clustering, Polarization, and Implications for Online Platforms

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Randomized trial investigates clustering and polarization in social networks, suggesting implications for online platforms.

This research aims to explain opinion polarization and clustering phenomena in social networks through a control-theoretic lens.
Developed two discrete-time models for opinion dynamics: an unsigned bounded-confidence model and a signed model for interactions.
Characterized interpersonal influence using edge-dependent confidence bounds in time-varying unsigned and signed digraphs.
Conducted numerical simulations, including a case study on the Slashdot Zoo social network with 50 controversial users.
Established conditions for invariant cluster consensus and balanced polarization under proposed models.
Demonstrated the significance of heterogeneous interpersonal influence on opinion evolution through simulations.
Illustrated the impact of platform-mediated trust on clustering and polarization dynamics.

Huang et al. (2026) studied this question.

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