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.

本文提出了一个基于 Monte Carlo 模拟的框架来评估人们推荐算法对意见演变的影响，结果发现人们推荐算法确实会导致峡谷效应显著增加，但仅在网络中有相当数量的同质性的情况下发生。