Cited by 381Ethics and Social Impacts of AISocial Media and PoliticsMisinformation and Its Impacts

The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index.

Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media

Open access
Swart, Joëlle·2021·Social Media + Society

Abstract

The news that young people consume is increasingly subject to algorithmic curation. Yet, while numerous studies explore how algorithms exert power in citizens’ everyday life, little is known about how young people themselves perceive, learn about, and deal with news personalization. Considering the interactions between algorithms and users from an user-centric perspective, this article explores how young people make sense of, feel about, and engage with algorithmic news curation on social media and when such everyday experiences contribute to their algorithmic literacy. Employing in-depth interviews in combination with the walk-through method and think-aloud protocols with a diverse group of 22 young people aged 16–26 years, it addresses three current methodological challenges to studying algorithmic literacy: first, the lack of an established baseline about how algorithms operate; second, the opacity of algorithms within everyday media use; and third, limitations in technological vocabularies that hinder young people in articulating their algorithmic encounters. It finds that users’ sense-making strategies of algorithms are context-specific, triggered by expectancy violations and explicit personalization cues. However, young people’s intuitive and experience-based insights into news personalization do not automatically enable young people to verbalize these, nor does having knowledge about algorithms necessarily stimulate users to intervene in algorithmic decisions.

Overview

Integrity signals

Registry

Crossref · 10.1177/20563051211008828

Cites

64 of 64 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 65 references

Research works printed but not linked: 4

  1. Cotter K., 2020, International Journal of Communication, 14, 745
  2. D’Ignazio C., 2015, Proceedings of the Bloomberg Data for Good Exchange Conference
  3. Klawitter E., 2018, International Journal of Communication, 12, 3490
  4. Schmidt J., 2019, International Journal of Communication, 13, 853

Cited by

Sorted by citations

Items 101–200 of 381

Metadata

Authors
Swart, J.
Affiliations
University of Groningen (NL)
Corresponding author
Swart, Joëlle
Venue
Social Media + Society
Published
2021-04
Volume
7
Issue
2
ISSN
2056-3051 (ISSN-L)
OpenAlex
W3152734712
Cited by
381
References
65
Citations by year
2026: 83 · 2025: 99 · 2024: 89 · 2023: 67 · 2022: 37 · 2021: 6
FWCI
62.21
Type
journal article
Language
en
Open access
Locations
  • Social Media + Society (publishedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
Faculty of Arts, University of Groningen (Writing up grant Faculty of Arts)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords
algorithmic literacy, personalized news, youth, social media, algorithmic opacity, expectancy violation, algorithmic intervention

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review