Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media
Open accessAbstract
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.
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Algorithms in search and social media limit user agency by making decisions about what information to display and filter out.
“Despite their helpfulness in limiting information overload in the domains of search and social media, these algorithms also limit user agency by making decisions about what information to display and filter out (Swart, 2021).”
From Expanding on the Frames: Making a Case for Algorithmic Literacy (2023) · cites this paper as Swart, 2021
A study found that although adolescents might understand that their actions influence the algorithm, thus determining the content they see, they often do not adjust their social media behaviors or clearly express their understanding of how algorithms work.
“A study found that, although adolescents might understand that their actions influence the algorithm, thus determining the content they see, they often do not adjust their social media behaviors or clearly express their understanding of how algorithms work [37].”
From Social Media Use in Adolescents: Bans, Benefits, and Emotion Regulation Behaviors (2024) · cites this paper as [37]
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When Stories Turn Institutional: How TikTok Users Legitimate the Algorithmic Sensemaking
Obreja, D. M. (2024) · Social Media + Society · doi:10.1177/20563051231224114 · Cited by 29 · see citing works
Exploring folk theories of algorithmic news curation for explainable design
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Generation Z and the Evolution of Social Media: A Two-Decade Analysis of Impact and Usage Trends
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How youth define, consume, and evaluate news: Reviewing two decades of research
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Balancing Realism and Trust: AI Avatars In Science Communication
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Algorithmic autobiographies and fictions: A digital method
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Social media and ADHD: implications for clinical assessment and treatment
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Volk, S. C., Schulz, A. M., Blassnig, S., Marschlich, S., Nguyen, M. H., & Strauß, N. (2024) · Information Communication & Society · doi:10.1080/1369118x.2024.2358167 · Cited by 22 · see citing works
Conceptualising the ‘algorithmic public opinion’: Public opinion formation in the digital age
Gandini, A., Keeling, S., & Reviglio, U. (2025) · Dialogues on Digital Society · doi:10.1177/29768640251323147 · Cited by 31 · see citing works
Anter, L., & Kümpel, A. S. (2023) · Digital Journalism · doi:10.1080/21670811.2023.2211635 · Cited by 19 · see citing works
Critical literacies in algorithmic cultures
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Perceived opacity leads to algorithm aversion in the workplace
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Algorithmic personalization: a study of knowledge gaps and digital media literacy
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News Personalization and Public Service Media: The Audience Perspective in Three European Countries
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Bubbles bursting: Investigating and measuring the personalisation of social media searches
Yang, C., Xu, X., Nunes, B. P., & Siqueira, S. W. M. (2023) · Telematics and Informatics · doi:10.1016/j.tele.2023.101999 · Cited by 16 · see citing works
If it is bad, why don’t I quit? Algorithmic recommendation use strategy from folk theories
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Bridging awareness and resistance: Using algorithmic knowledge against controversial content
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Li, H., Chen, B., Chen, Z., Chen, L., Zhang, D. “., Wen, M., Li, J., Li, Y., Han, X., & Su, D. (2023) · Journal of Health Communication · doi:10.1080/10810730.2023.2245373 · Cited by 16 · see citing works
Expanding on the Frames: Making a Case for Algorithmic Literacy
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Huang, G. I., Wong, I. A., Torres, W. C. Z., Davari, D., & Xie, H. (2024) · Tourism Management · doi:10.1016/j.tourman.2024.105075 · Cited by 15 · see citing works
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If you’re reading this, it’s meant for you: The reflexive ambivalence of algorithmic conspirituality
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Artificial intelligence and the dawn of an algorithmic divide
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Policy responses to false and misleading digital content
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Confusing Content, Platforms, and Data: Young Adults and Trust in News Media
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Contesting personalized recommender systems: a cross-country analysis of user preferences
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A Comparative Study on AI-Based Learning Behaviors: Evidence from Vietnam
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Role of Algorithm Awareness in Privacy Decision-Making Process: A Dual Calculus Lens
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Navigating open-source platforms in schools: an inquiry into changing teacher professionality
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Fang, W., & Jin, J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.953892 · Cited by 13 · see citing works
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- Authors
- Swart, J.
- Affiliations
- University of Groningen (NL)
- Corresponding author
- Swart, Joëlle
- ORCID
- Swart, Joëlle
- Venue
- Social Media + Society
- Publisher
- SAGE Publications
- 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
- gold
- doi.org
- 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
- Social Sciences › Safety Research › 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.
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