Evidence map›Paper›PMID 39270019›Full record

ArticleScience advances2024

Ideological self-selection in online news exposure: Evidence from Europe and the US.

Frank Mangold, David Schoch, Sebastian Stier

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. Review
  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.

Frank MangoldDepartment Computational Social Science, GESIS - Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, 50667 Cologne, Germany.ORCID 0000-0002-9776-3113
David SchochDepartment Computational Social Science, GESIS - Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, 50667 Cologne, Germany.ORCID 0000-0003-2952-4812
Sebastian StierDepartment Computational Social Science, GESIS - Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, 50667 Cologne, Germany.ORCID 0000-0002-1217-5778

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Today's high-choice digital media environments allow citizens to completely refrain from online news exposure and, if they do use news, to select sources that align with their ideological preferences. Yet due to measurement problems and cross-country differences, recent research has been inconclusive regarding the prevalence of ideological self-selection into like-minded online news. We introduce a multi-method design combining the web-browsing histories and survey responses of more than 7000 participants from six major democracies with supervised text classification to separate political from nonpolitical news exposure. We find that political online news exposure is both substantially less prevalent and subject to stronger ideological self-selection than nonpolitical online news exposure, especially in the United States. By highlighting the peculiar role of political news content, the results improve the understanding of online news exposure and the role of digital media in democracy.

Indexed as

InternetPoliticsEuropeHumansSurveys and QuestionnairesUnited States

Identifiers

PMID39270019
PMCPMC11397479

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.