Local Edge Dynamics and Opinion Polarization

TL;DR

This paper investigates how local edge dynamics, such as confirmation bias and friend-of-friend recommendations, influence opinion polarization in social networks through a modified opinion model and real-world data validation.

Contribution

It introduces a novel variant of Friedkin-Johnsen opinion dynamics incorporating evolving network edges, providing insights into polarization mechanisms.

Findings

Edge dynamics increase opinion polarization.

Confirmation bias and friend-of-friend links promote echo chambers.

Model aligns synthetic graphs with real social network structures.

Abstract

The proliferation of social media platforms, recommender systems, and their joint societal impacts have prompted significant interest in opinion formation and evolution within social networks. We study how local edge dynamics can drive opinion polarization. In particular, we introduce a variant of the classic Friedkin-Johnsen opinion dynamics, augmented with a simple time-evolving network model. Edges are iteratively added or deleted according to simple rules, modeling decisions based on individual preferences and network recommendations. Via simulations on synthetic and real-world graphs, we find that the combined presence of two dynamics gives rise to high polarization: 1) confirmation bias -- i.e., the preference for nodes to connect to other nodes with similar expressed opinions and 2) friend-of-friend link recommendations, which encourage new connections between closely connected nodes. We show that our model is tractable to theoretical analysis, which helps explain how these local dynamics erode connectivity across opinion groups, affecting polarization and a related measure of disagreement across edges. Finally, we validate our model against real-world data, showing that our edge dynamics drive the structure of arbitrary graphs, including random graphs, to more closely resemble real social networks.