# Modeling echo chambers and polarization dynamics in social networks

arXiv: https://arxiv.org/abs/1906.12325 · HTML: https://mlsanity.com/paper/1906.12325/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

## Built on by (most cited first, 16 of 16)

- [Y Social: an LLM-powered Social Media Digital Twin](https://mlsanity.com/paper/2408.00818/) (2024)
- [Steering Language Models with Game-Theoretic Solvers](https://mlsanity.com/paper/2402.01704/) (2024)
- [A Simulation System Towards Solving Societal-Scale Manipulation](https://mlsanity.com/paper/2410.13915/) (2024)
- [Polarization of Autonomous Generative AI Agents Under Echo Chambers](https://mlsanity.com/paper/2402.12212/) (2024)
- [Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion](https://mlsanity.com/paper/2212.09056/) (2022)
- [In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions](https://mlsanity.com/paper/2412.14414/) (2024)
- [Dynamics of Algorithmic Content Amplification on TikTok](https://mlsanity.com/paper/2503.20231/) (2025)
- [Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach](https://mlsanity.com/paper/2307.04668/) (2023)
- [Integrating LLM and Diffusion-Based Agents for Social Simulation](https://mlsanity.com/paper/2510.16366/) (2025)
- [PIE: Personalized Interest Exploration for Large-Scale Recommender Systems](https://mlsanity.com/paper/2304.06844/) (2023)
- [All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media](https://mlsanity.com/paper/2312.04603/) (2023)
- [Nonlinear dynamical social and political prediction algorithm for city planning and public participation using the Impulse Pattern Formulation](https://mlsanity.com/paper/2404.00977/) (2024)
- [Generating consensus and dissent on massive discussion platforms with a semantic-vector model](https://mlsanity.com/paper/2601.13932/) (2026)
- [k-hop Fairness: Addressing Disparities in Graph Link Prediction Beyond First-Order Neighborhoods](https://mlsanity.com/paper/2603.03867/) (2026)
- [Multiscale Exit-Join Dynamics: Tactical Consensus and Strategic Coalition Formation](https://mlsanity.com/paper/2606.26139/) (2026)
- [A Social Media Analysis of Discourse on the Israel--Palestine Conflict on Telegram](https://mlsanity.com/paper/2608.21385/) (2026)

## Abstract (arXiv)

Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on the spread of misinformation and on openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena stay unclear. We propose a model that introduces the dynamics of radicalization, as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users' engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media.