# Cascade-based Echo Chamber Detection

arXiv: https://arxiv.org/abs/2208.04620 · HTML: https://mlsanity.com/paper/2208.04620/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [The Effect of People Recommenders on Echo Chambers and Polarization](https://mlsanity.com/paper/2112.00626/) (2021)
- [Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media](https://mlsanity.com/paper/1908.03146/) (2019)
- [Learning Opinion Dynamics From Social Traces](https://mlsanity.com/paper/2006.01673/) (2020)
- [Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter](https://mlsanity.com/paper/2003.11906/) (2020)
- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [Modeling confirmation bias and polarization](https://mlsanity.com/paper/1607.00022/) (2016)
- [Personality Traits and Echo Chambers on Facebook](https://mlsanity.com/paper/1606.04721/) (2016)
- [No Echo in the Chambers of Political Interactions on Reddit](https://mlsanity.com/paper/2102.05477/) (2021)
- [RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions](https://mlsanity.com/paper/2101.04751/) (2021)
- [Learning Ideological Embeddings from Information Cascades](https://mlsanity.com/paper/2109.13589/) (2021)
- [Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways](https://mlsanity.com/paper/2202.00640/) (2022)
- [Quantifying echo chamber effects in information spreading over political communication networks](https://mlsanity.com/paper/1901.03688/) (2019)
- [Mechanisms and Attributes of Echo Chambers in Social Media](https://mlsanity.com/paper/2106.05401/) (2021)
- [When Less Is More: Systematic Analysis of Cascade-Based Community Detection](https://mlsanity.com/paper/2002.00840/) (2020)

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

- [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)
- [Variational Inference of Parameters in Opinion Dynamics Models](https://mlsanity.com/paper/2403.05358/) (2024)
- [Rebalancing Social Feed to Minimize Polarization and Disagreement](https://mlsanity.com/paper/2308.14486/) (2023)
- [Reducing Exposure to Harmful Content via Graph Rewiring](https://mlsanity.com/paper/2306.07930/) (2023)
- [Evaluating LLMs Capabilities Towards Understanding Social Dynamics](https://mlsanity.com/paper/2411.13008/) (2024)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)

## Abstract (arXiv)

Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing. In this work, we aim to fill this gap by proposing a probabilistic generative model that explains social media footprints -- i.e., social network structure and propagations of information -- through a set of latent communities, characterized by a degree of echo-chamber behavior and by an opinion polarity. Specifically, echo chambers are modeled as communities that are permeable to pieces of information with similar ideological polarity, and impermeable to information of opposed leaning: this allows discriminating echo chambers from communities that lack a clear ideological alignment. To learn the model parameters we propose a scalable, stochastic adaptation of the Generalized Expectation Maximization algorithm, that optimizes the joint likelihood of observing social connections and information propagation. Experiments on synthetic data show that our algorithm is able to correctly reconstruct ground-truth latent communities with their degree of echo-chamber behavior and opinion polarity. Experiments on real-world data about polarized social and political debates, such as the Brexit referendum or the COVID-19 vaccine campaign, confirm the effectiveness of our proposal in detecting echo chambers. Finally, we show how our model can improve accuracy in auxiliary predictive tasks, such as stance detection and prediction of future propagations.