The Effect of People Recommenders on Echo Chambers and Polarization

TL;DR

This paper presents a simulation framework to evaluate how people-recommender systems influence the formation of echo chambers and polarization on social media, revealing that recommenders can increase echo chambers especially in initially homophilic networks.

Contribution

The paper introduces a novel Monte Carlo simulation framework combining link recommendation and opinion dynamics to study social media polarization effects.

Findings

Recommenders can significantly increase echo chambers in highly homophilic networks.

The effect of recommenders is negligible if echo chambers already exist.

Results are consistent across different opinion dynamics models.

Abstract

The effects of social media on critical issues, such as polarization and misinformation, are under scrutiny due to the disruptive consequences that these phenomena can have on our societies. Among the algorithms routinely used by social media platforms, people-recommender systems are of special interest, as they directly contribute to the evolution of the social network structure, affecting the information and the opinions users are exposed to. In this paper, we propose a framework to assess the effect of people recommenders on the evolution of opinions. Our proposal is based on Monte Carlo simulations combining link recommendation and opinion-dynamics models. In order to control initial conditions, we define a random network model to generate graphs with opinions, with tunable amounts of modularity and homophily. We join these elements into a methodology to study the effects of the recommender system on echo chambers and polarization. We also show how to use our framework to measure, by means of simulations, the impact of different intervention strategies. Our thorough experimentation shows that people recommenders can in fact lead to a significant increase in echo chambers. However, this happens only if there is considerable initial homophily in the network. Also, we find that if the network already contains echo chambers, the effect of the recommendation algorithm is negligible. Such findings are robust to two very different opinion dynamics models, a bounded confidence model and an epistemological model.