Evidence map›Paper›PMID 39475604›Full record

ArticleScience advances2024

Current engagement with unreliable sites from web search driven by navigational search.

Kevin T Greene, Nilima Pisharody, Lucas Augusto Meyer, Mayana Pereira, Rahul Dodhia, Juan Lavista Ferres, Jacob N Shapiro

Abstract read
In one paragraph

Article in Science advances, 2024. 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. 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

7 authors.

Kevin T GreeneEmpirical Studies of Conflict Project, Princeton University, Princeton, NJ, USA.ORCID 0000-0003-4834-3996
Nilima PisharodyEmpirical Studies of Conflict Project, Princeton University, Princeton, NJ, USA.ORCID 0009-0004-0511-2981
Lucas Augusto MeyerAI for Good Research Lab, Microsoft, Redmond, WA, USA.ORCID 0000-0001-7647-3451
Mayana PereiraAI for Good Research Lab, Microsoft, Redmond, WA, USA.ORCID 0000-0001-8636-8882
Rahul DodhiaAI for Good Research Lab, Microsoft, Redmond, WA, USA.ORCID 0000-0003-3812-3906
Juan Lavista FerresAI for Good Research Lab, Microsoft, Redmond, WA, USA.ORCID 0000-0002-9654-3178
Jacob N ShapiroEmpirical Studies of Conflict Project, Princeton University, Princeton, NJ, USA.ORCID 0000-0002-7484-229X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Do search engine algorithms systematically expose users to content from unreliable sites? There is widespread concern that they do, but little systematic evidence that search engine algorithms, rather than user-expressed preferences, are driving current exposure to and engagement with unreliable information sources. Using two datasets totaling roughly 14 billion search engine result pages (SERPs) from Bing, the second most popular search engine in the U.S., we show that search exposes users to few unreliable information sources. The vast majority of engagement with unreliable information sources from search occurs when users are explicitly searching for information from those sites, despite those searches being an extremely small share of the overall search volume. Our findings highlight the importance of accounting for user preference when examining engagement with unreliable sources from web search.

Indexed as

AlgorithmsInternetSearch EngineHumans

Identifiers

PMID39475604
PMCPMC11524180

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.