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Preprints, Working Papers, ... Year : 2023

Is Twitter's recommender biased ? An audit

Le système de recommandation de Twitter est-il biaisé ? Un audit

Abstract

Combining crowd-sourced data donation and a largescale server-side data collection, we provide quantitative experimental evidence of Twitter recommender distortion of users' environment reality. Twitter's algorithmically curated home feed amplifies toxic and sentimentally valenced tweets, distorts the political landscape perceived by the users, and favors small and/or usually quiet accounts. We argue the need of independent audits of social media platforms with access to large-scale data.
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Dates and versions

hal-04036232 , version 1 (19-03-2023)
hal-04036232 , version 2 (24-03-2023)
hal-04036232 , version 3 (03-07-2023)
hal-04036232 , version 4 (01-10-2023)

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  • HAL Id : hal-04036232 , version 1

Cite

Paul Bouchaud, David Chavalarias, Maziyar Panahi. Is Twitter's recommender biased ? An audit. 2023. ⟨hal-04036232v1⟩
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