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Journal Articles Scientific Reports Year : 2023

Crowdsourced Audit of Twitter’s Recommender Systems

Audit crowdsourcé du système de recommandation de Twitter


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.
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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)



Paul Bouchaud, David Chavalarias, Maziyar Panahi. Crowdsourced Audit of Twitter’s Recommender Systems. Scientific Reports, 2023, 13 (1), ⟨10.1038/s41598-023-43980-4⟩. ⟨hal-04036232v4⟩
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