The Effect of People Recommenders on Echo Chambers and Polarization


  • Federico Cinus Sapienza University, Rome, Italy ISI Foundation, Turin, Italy
  • Marco Minici University of Pisa, Italy ICAR-CNR, Rende, Italy
  • Corrado Monti ISI Foundation, Turin, Italy
  • Francesco Bonchi Centai, Turin, Italy Eurecat, Barcelona, Spain



Organizational and group behavior mediated by social media; interpersonal communication mediated by social media, Qualitative and quantitative studies of social media, Human computer interaction; social media tools; navigation and visualization, Social innovation and effecting change through social media


The effects of online 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 novel 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. Finally, we join these elements into a methodology able to study the causal relationship between the recommender system and the echo chamber effect. Our method can also assess if such relationships are statistically significant. We also show how such a framework can be used 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.




How to Cite

Cinus, F., Minici, M., Monti, C., & Bonchi, F. (2022). The Effect of People Recommenders on Echo Chambers and Polarization. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 90-101.