Combining opinion and structural similarity in link recommendations to counter extreme polarization

TL;DR

This paper investigates how combining opinion and structural similarity in link recommendations influences social network polarization, showing that different mechanisms lead to varying degrees of fragmentation and opinion diversity.

Contribution

It introduces a co-evolution model of opinions and network structure considering both similarity metrics, revealing their distinct impacts on polarization and network fragmentation.

Findings

Opinion similarity increases opinion diversity.

Structural similarity promotes network fragmentation.

Weak structural dependence prevents fragmentation and supports moderation.

Abstract

Recommendation algorithms, used in online social networks, shape interactions between users. In particular, link-recommendation algorithms suggest new connections and affect how individuals interact and exchange information. These algorithms' efficacy relies on key mechanisms governing the creation of social ties, such as triadic closure and homophily. The first is achieved through structural similarity and represents a heightened chance of recommending users to one another given mutual friends; the second is related to opinion similarity and conveys an increased chance of recommending a connection given similar individual characteristics. These two mechanisms jointly shape the evolution of social networks and behaviors unfolding over them. Their combined effect on the co-evolution of opinion and structure dynamics remains, however, poorly understood. Here, we study how social networks and opinions co-evolve given the joint effect of rewiring based on opinion and structural similarity. We show that both similarity metrics lead to polarized states, but differ in how they impact network fragmentation and opinion diversity. While strongly relying on opinion similarity leads to a higher variation of opinion, rewiring via network similarity leads to a larger number of (dis)connected components, resulting in fragmented networks that lean towards one of the signed opinions. Under strong homophilic settings, introducing a weak dependence on structural similarity prevents network fragmentation and favors moderate opinions. This work can inform the design of new recommender algorithms that explicitly account for interacting social and recommendation mechanisms, with the potential to foster moderate opinion coexistence even in inherently polarizing settings.