Entropy-based detection of Twitter echo chambers.
PNAS Nexus
; 3(5): pgae177, 2024 May.
Article
en En
| MEDLINE
| ID: mdl-38737768
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.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
PNAS Nexus
Año:
2024
Tipo del documento:
Article
País de afiliación:
Italia