Crowdsourced audit of Twitter's recommender systems.

Bouchaud, Paul; Chavalarias, David; Panahi, Maziyar

Bouchaud, Paul; Chavalarias, David; Panahi, Maziyar.

Afiliación

Bouchaud P; CNRS, Complex Systems Institute of Paris Île-de-France (ISC-PIF), 75013, Paris, France. paul.bouchaud@iscpif.fr.
Chavalarias D; EHESS, Center for Social Analysis and Mathematics (CAMS), 75006, Paris, France. paul.bouchaud@iscpif.fr.
Panahi M; CNRS, Complex Systems Institute of Paris Île-de-France (ISC-PIF), 75013, Paris, France.

Sci Rep ; 13(1): 16815, 2023 10 05.

Article en En | MEDLINE | ID: mdl-37798318

RESUMEN

This research conducts an audit of Twitter's recommender system, aiming to examine the disparities between users' curated timelines and their subscription choices. Through the combined use of a browser extension and data collection via the Twitter API, our investigation reveals a high amplification of friends from the same community, a preference for amplifying emotionally charged and toxic tweets and an uneven algorithmic amplification across friends' political leaning. This audit emphasizes the importance of transparency, and increased awareness regarding the impact of algorithmic curation.

Asunto(s)

Colaboración de las Masas; Medios de Comunicación Sociales; Humanos; Comunicación

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Medios de Comunicación Sociales / Colaboración de las Masas Límite: Humans Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Reino Unido

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