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

TL;DR

This paper introduces ECS, a new metric for quantifying online echo chambers by measuring user embedding distances without requiring ideological labels, using a self-supervised graph autoencoder.

Contribution

It proposes ECS and EchoGAE, novel methods for measuring and embedding user ideological similarity without labels or graph assumptions.

Findings

ECS effectively quantifies echo chambers in social media data.

EchoGAE produces embeddings reflecting ideological similarities.

ECS distinguishes polarizing from non-polarizing topics.

Abstract

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.