# Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach

arXiv: https://arxiv.org/abs/2307.04668 · HTML: https://mlsanity.com/paper/2307.04668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 10 Jul 2023.

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

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Overcoming Language Variation in Sentiment Analysis with Social Attention](https://mlsanity.com/paper/1511.06052/) (2015)
- [Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm](https://mlsanity.com/paper/1703.09307/) (2017)
- [Minimizing Polarization and Disagreement in Social Networks](https://mlsanity.com/paper/1712.09948/) (2017)
- [Modeling echo chambers and polarization dynamics in social networks](https://mlsanity.com/paper/1906.12325/) (2019)
- [Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks](https://mlsanity.com/paper/2207.08349/) (2022)
- [Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter](https://mlsanity.com/paper/2003.11906/) (2020)
- [Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey](https://mlsanity.com/paper/2005.09649/) (2020)
- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [Measuring Political Polarization: Twitter shows the two sides of Venezuela](https://mlsanity.com/paper/1505.04095/) (2015)
- [Who Falls for Online Political Manipulation?](https://mlsanity.com/paper/1808.03281/) (2018)
- [Quantifying echo chamber effects in information spreading over political communication networks](https://mlsanity.com/paper/1901.03688/) (2019)
- [A Framework for Quantifying Controversy of Social Network Debates Using Attributed Networks: Biased Random Walk (BRW)](https://mlsanity.com/paper/2008.11453/) (2020)

## Built on by (most cited first, 1 of 1)

- [Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks](https://mlsanity.com/paper/2609.06521/) (2026)

## Abstract (arXiv)

The rise of social media platforms has facilitated the formation of echo chambers, which are online spaces where users predominantly encounter viewpoints that reinforce their existing beliefs while excluding dissenting perspectives. This phenomenon significantly hinders information dissemination across communities and fuels societal polarization. Therefore, it is crucial to develop methods for quantifying echo chambers. In this paper, we present the Echo Chamber Score (ECS), a novel metric that assesses the cohesion and separation of user communities by measuring distances between users in the embedding space. In contrast to existing approaches, ECS is able to function without labels for user ideologies and makes no assumptions about the structure of the interaction graph. To facilitate measuring distances between users, we propose EchoGAE, a self-supervised graph autoencoder-based user embedding model that leverages users' posts and the interaction graph to embed them in a manner that reflects their ideological similarity. To assess the effectiveness of ECS, we use a Twitter dataset consisting of four topics - two polarizing and two non-polarizing. Our results showcase ECS's effectiveness as a tool for quantifying echo chambers and shedding light on the dynamics of online discourse.