# The Effect of People Recommenders on Echo Chambers and Polarization

arXiv: https://arxiv.org/abs/2112.00626 · HTML: https://mlsanity.com/paper/2112.00626/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2021.

## Built on by (most cited first, 20 of 20)

- [Y Social: an LLM-powered Social Media Digital Twin](https://mlsanity.com/paper/2408.00818/) (2024)
- [AI and the Problem of Knowledge Collapse](https://mlsanity.com/paper/2404.03502/) (2024)
- [Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks](https://mlsanity.com/paper/2409.19338/) (2024)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [A Simulation System Towards Solving Societal-Scale Manipulation](https://mlsanity.com/paper/2410.13915/) (2024)
- [Cascade-based Echo Chamber Detection](https://mlsanity.com/paper/2208.04620/) (2022)
- [Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways](https://mlsanity.com/paper/2202.00640/) (2022)
- [Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences](https://mlsanity.com/paper/2409.16478/) (2024)
- [Engagement-Driven Content Generation with Large Language Models](https://mlsanity.com/paper/2411.13187/) (2024)
- [You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control](https://mlsanity.com/paper/2507.16829/) (2025)
- [Rebalancing Social Feed to Minimize Polarization and Disagreement](https://mlsanity.com/paper/2308.14486/) (2023)
- [DURENDAL: Graph deep learning framework for temporal heterogeneous networks](https://mlsanity.com/paper/2310.00336/) (2023)
- [Delayed and Indirect Impacts of Link Recommendations](https://mlsanity.com/paper/2303.09700/) (2023)
- [PIE: Personalized Interest Exploration for Large-Scale Recommender Systems](https://mlsanity.com/paper/2304.06844/) (2023)
- [Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions](https://mlsanity.com/paper/2412.03600/) (2024)
- [Opinion de-polarization in social networks with GNNs](https://mlsanity.com/paper/2412.09404/) (2024)
- [The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation](https://mlsanity.com/paper/2504.07911/) (2025)
- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)
- [LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems](https://mlsanity.com/paper/2606.02883/) (2026)
- [Asymmetric Discourse Homogenization and Shared Language Technology: Evidence from Reddit](https://mlsanity.com/paper/2608.13674/) (2026)

## Abstract (arXiv)

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.