Entropy-based detection of Twitter echo chambers

TL;DR

This paper introduces an unbiased entropy-based method to detect echo chambers on Twitter, revealing their limited presence but significant influence and association with disinformation in the context of Covid-19 vaccination debates.

Contribution

The paper presents a novel, data-agnostic entropy-based approach for identifying echo chambers, applicable across different datasets and social media contexts.

Findings

Echo chambers constitute about 0.35% of users in the dataset.

Users in echo chambers account for nearly one-third of retweets.

Echo chambers are associated with disinformative content.

Abstract

Echo chambers, i.e. clusters of users exposed to news and opinions in line with their previous beliefs, were observed in many online debates on social platforms. We propose a completely unbiased entropy-based method for detecting echo chambers. The method is completely agnostic to the nature of the data. In the Italian Twitter debate about the Covid-19 vaccination, we find a limited presence of users in echo chambers (about 0.35% of all users). Nevertheless, their impact on the formation of a common discourse is strong, as users in echo chambers are responsible for nearly a third of the retweets in the original dataset. Moreover, in the case study observed, echo chambers appear to be a receptacle for disinformative content.