# Users Polarization on Facebook and Youtube

arXiv: https://arxiv.org/abs/1604.02705 · HTML: https://mlsanity.com/paper/1604.02705/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2016.

## Built on by (most cited first, 10 of 10)

- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [The Lock-in Hypothesis: Stagnation by Algorithm](https://mlsanity.com/paper/2506.06166/) (2025)
- [More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram](https://mlsanity.com/paper/2410.08642/) (2024)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [The Risks of Machine Learning Systems](https://mlsanity.com/paper/2204.09852/) (2022)
- [Rebalancing Social Feed to Minimize Polarization and Disagreement](https://mlsanity.com/paper/2308.14486/) (2023)
- [Emergent Instabilities in Algorithmic Feedback Loops](https://mlsanity.com/paper/2201.07203/) (2022)
- [On the Complexity of the Bipartite Polarization Problem: from Neutral to Highly Polarized Discussions](https://mlsanity.com/paper/2307.11621/) (2023)
- [Measuring Narrative Polarization in Online Discourse](https://mlsanity.com/paper/2601.07398/) (2026)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)

## Abstract (arXiv)

On social media algorithms for content promotion, accounting for users preferences, might limit the exposure to unsolicited contents. In this work, we study how the same contents (videos) are consumed on different platforms -- i.e. Facebook and YouTube -- over a sample of 12M of users. Our findings show that the same content lead to the formation of echo chambers, irrespective of the online social network and thus of the algorithm for content promotion. Finally, we show that the users' commenting patterns are accurate early predictors for the formation of echo-chambers.