Evidence map›Paper›PMID 37798318›Full record

ArticleScientific reports2023

Crowdsourced audit of Twitter's recommender systems.

Paul Bouchaud, David Chavalarias, Maziyar Panahi

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Value misalignments in X's feed algorithm is a reflection of value tensions in engagement.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Paul BouchaudCNRS, Complex Systems Institute of Paris Île-de-France (ISC-PIF), 75013, Paris, France. paul.bouchaud@iscpif.fr.ORCID http://orcid.org/0000-0002-0310-8194
David ChavalariasCNRS, Complex Systems Institute of Paris Île-de-France (ISC-PIF), 75013, Paris, France.ORCID http://orcid.org/0000-0001-9485-1399
Maziyar PanahiCNRS, Complex Systems Institute of Paris Île-de-France (ISC-PIF), 75013, Paris, France.ORCID http://orcid.org/0000-0002-3060-9883

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

CrowdsourcingSocial MediaCommunicationHumans

Identifiers

PMID37798318
PMCPMC10556069

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.