# Social Influence and Unfollowing Accelerate the Emergence of Echo Chambers

arXiv: https://arxiv.org/abs/1905.03919 · HTML: https://mlsanity.com/paper/1905.03919/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 7 of 7)

- [Promotional Language and the Adoption of Innovative Ideas in Science](https://mlsanity.com/paper/2406.02798/) (2024)
- [Large Language Model Driven Agents for Simulating Echo Chamber Formation](https://mlsanity.com/paper/2502.18138/) (2025)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Community Moderation and the New Epistemology of Fact Checking on Social Media](https://mlsanity.com/paper/2505.20067/) (2025)
- [DynamiX: Large-Scale Dynamic Social Network Simulator](https://mlsanity.com/paper/2507.19929/) (2025)
- [Morality-based Assertion and Homophily on Social Media: A Cultural Comparison between English and Japanese Languages](https://mlsanity.com/paper/2108.10643/) (2021)
- [Simulating Hate Speech Cascades with Multi-LLM Agents: Empirical Grounding, Modeling Fidelity, and Intervention Strategies](https://mlsanity.com/paper/2606.18264/) (2026)

## Abstract (arXiv)

While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as "echo chambers." Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.