The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed
Abstract
People are becoming increasingly reliant on online socio-technical systems that employ algorithmic curation to organize, select and present information. We wanted to understand how individuals make sense of the influence of algorithms, and how awareness of algorithmic curation may impact their interaction with these systems. We investigated user understanding of algorithmic curation in Facebook's News Feed, by analyzing open-ended responses to a survey question about whether respondents believe their News Feeds show them every post their Facebook Friends create. Responses included a wide range of beliefs and causal inferences, with different potential consequences for user behavior in the system. Because user behavior is both input for algorithms and constrained by them, these patterns of belief may have tangible consequences for the system as a whole.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“The algorithms embedded in social media adjust the content they feed each particular user such that each user will remain engaged with the platform for ever longer periods of time”
From Ethics of the Attention Economy: The Problem of Social Media Addiction (2021) · cites this paper as Rader & Gray, 2015
In the citing paper
“Social media companies use so-called adaptive algorithms that continuously refine their platforms such that they can become more addictive for each user.”
From Ethics of the Attention Economy: The Problem of Social Media Addiction (2021) · cites this paper as Rader & Gray, 2015
Citations
Cites
Experimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320040111 · Cited by 3,687
(2009) · Choice Reviews Online · doi:10.5860/choice.46-6156 · Cited by 3,084
The role of social networks in information diffusion
Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187907 · Cited by 1,526
Looking at, looking up or keeping up with people?: motives and use of facebook
Joinson, A. N. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357213 · Cited by 1,393
Wright, D., & Meadows, D. H. (2012) · Routledge eBooks · doi:10.4324/9781849773386 · Cited by 1,373
Want to be on the top? Algorithmic power and the threat of invisibility on Facebook
Bucher, T. (2012) · New Media & Society · doi:10.1177/1461444812440159 · Cited by 1,305
Patterson, T. E. (1993) · Medical Entomology and Zoology · Cited by 1,293
Mental Models: An Interdisciplinary Synthesis of Theory and Methods
Jones, N. A., Ross, H., Lynam, T., Perez, P., & Leitch, A. (2011) · Ecology and Society · doi:10.5751/es-03802-160146 · Cited by 1,055
Gillespie, T. (2014) · Media Technologies · doi:10.7551/mitpress/9042.003.0013 · Cited by 1,016
Sociotechnical principles for system design
Clegg, C. (2000) · Applied Ergonomics · doi:10.1016/s0003-6870(00)00009-0 · Cited by 846
Flagged first, then by citations
Research works printed but not linked: 3
- Backstrom L., 2013, Aug.
- Lenhart A., 2009, Adults and social network sites. Tech. rep
- Sharma A., 2013, ICWSM '13
Cited by
Sorted by citations
Items 301–384 of 384
Us Vs. Them – Understanding the Impact of Homophily in Political Discussions on Twitter
Hettiachchi, D., Arora, T., & Gonçalves, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85610-6_27 · Cited by 2
“user control on information feeds, the algorithms dictate the user experience of information consumption on social media [33, 68].”
BackgroundThe power of algorithms and keys of participation
Esteves, J. (2022) · Routledge eBooks · doi:10.4324/9781003134176-4 · Cited by 2
Penguatan Literasi Digital Pada Generasi Millenial Dalam Menyongsong Pemilihan Umum 2024
Yunas, N. S., Said, M. F., & Aziz, A. (2023) · Surya Abdimas · doi:10.37729/abdimas.v7i4.3432 · Cited by 1
Analysing Coping Strategies of Teenage Girls Towards Instagram’s Algorithmic Bias
Constant, I., Tsibolane, P., Budree, A., & Oosterwyk, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61281-7_10 · Cited by 1
Critical Postdigital Literacies as Resistance Against Platform Governance on Twitter
Hagh, A. (2024) · Postdigital Science and Education · doi:10.1007/s42438-024-00495-z · Cited by 1
“This dynamism that marks the modern-day Web has prompted greater investigation into the relationship(s) between humans, platforms, and algorithms (Rader and Gray 2015; Siles et al.”
How Do Social Media Algorithms Appear? A Phenomenological Response to the Black Box Metaphor
Longo, A. (2025) · Minds and Machines · doi:10.1007/s11023-025-09716-1 · Cited by 3
“Moreover, more recent studies indicate a growing awareness of algorithms, with some studies noting a clear majority (Rader & Gray, 2015) or even all participants being aware of algorithmic curation (Karizat et al., 2021, p. 24).”
Introduction: Libraries and Librarians as Agents of Health Information Justice
Jean, B. S., Jaeger, P. T., Jindal, G., et al. (2020) · Emerald Group Publishing Limited eBooks · doi:10.1108/s0065-283020200000047001 · Cited by 1
Register, Y., & Spiro, E. S. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19337 · Cited by 1
“Algorithms that users may not even realize are powering their experience can be especially fruitful targets (Rader and Gray 2015).”
BackgroundUnderstanding User Experience with Recommendations in Social Network Service Feed
Kwak, D., Kim, K., & Lim, Y.-K. (2022) · [ ] With Design: Reinventing Design Modes · doi:10.1007/978-981-19-4472-7_190 · Cited by 1
How does the User's Knowledge of the Recommender Influence their Behavior?Preprint
Ghori, M. F., Dehpanah, A., Gemmell, J. F., Qahri‐Saremi, H., & Mobasher, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.00982 · Cited by 1
“Rader and Gray [9] investigated user understanding of algorithmic curation in Facebook’s news feed.”
Background(Why) Is misinformation a problem?Preprint
Osman, M., Adams, Z., Meder, B., & Bechlivanidis, C. (2022) · doi:10.31234/osf.io/xvhbs · Cited by 1
The Silencing Power of Algorithms
Morgan, C. J. (2018) · PDXScholar (Portland State University) · doi:10.15760/honors.1245 · Cited by 1
Decision Biases in User Agreement with Intelligent Decision Aids
Solomon, J. (2015) · Michigan State University Libraries · doi:10.25335/fsx1-c960
Leveraging Mixed Expertise in Crowdsourcing.
Merritt, D. C. (2016) · Deep Blue (University of Michigan)
Papsdorf, C., & Jakob, S. (2017) · kommunikation gesellschaft · doi:10.15460/kommges.2017.18.2.587
Ranking Content based on Semantic Dimensions
Cohn, J., Muthukumaran, S., & Birnbaum, L. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3120992
“When Facebook’s NewsFeed algorithm recommends content, it is relying almost entirely on metrics based on interactions in the social network itself, such as number of likes, the people involved in the post, and those people’s closeness to you [5].”
BackgroundWilliams, J. (2018)
The People Behind the Press: Building Social Capital in Networked News Models
Fox, S. J. (2017) · Queensland University of Technology · doi:10.5204/thesis.eprints.114128
Williams, J. (2018)
Liao, T. (2019) · AoIR Selected Papers of Internet Research · doi:10.5210/spir.v2019i0.11001
“While the accuracy of these predictive algorithms is still unknown, users are increasingly becoming aware of these algorithms and their use in behavioral advertising (Rader & Gray, 2015; Ur et al., 2012).”
Background"A cold, technical decision-maker": Can AI provide explainability, negotiability, and humanity?Preprint
Woodruff, A., Anderson, Y. A., Armstrong, K. J., Gkiza, M., Jennings, J., Moessner, C., Viégas, F. B., WATTENBERG, M. P., Webb, A. L., Wrede, F., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00874 · Cited by 10
“Beyond the challenges described above, provision of good explanations is further complicated by the fact that end users often have fundamental questions or misconceptions about the operation of algorithmic systems [12, 34, 78, 90, 97], and the construction of useful explanations remains elusive [68, 77].”
BackgroundGómez, J. C. S. (2021) · Hipertextos · doi:10.24215/23143924e029
Jardine, E. (2021) · Journal of Cyber Policy · doi:10.1080/23738871.2021.1953557
van de Wiel, T. (2021) · Utrecht University Repository (Utrecht University)
The algorithmic tyranny of perceived opinion
Sætra, H. S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823806-6.00007-7
Book Review: The Social Media Age by Zoetanya Sujon
Fowler, J. G., & Fowler, A. R. (2022) · Journal of Macromarketing · doi:10.1177/02761467221081226
“There is often a disconnect between the various ways that algorithmi-cally based systems function and the social interpretations of their impact (e.g., Ananny 2011; Gillespie 2011; Rader and gray 2015).”
BackgroundSwart, J. (2022) · Intellect Books · doi:10.1386/9781789386493_12
An interdisciplinary examination of partisanship in web search
Robertson, R. E. (2021) · Digital Repository Service (Northeastern University) · doi:10.17760/d20413988
AI & Human Ranking It Is Here, Can We Live with It?Preprint
Greenbaum, D. S., Cousens, D. E., Habas, D., & Baran, O. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4194107
Business Tech in Media and Entertainment
Chopra, S., & Nagaraj, S. (2022) · Advances in business information systems and analytics book series · doi:10.4018/978-1-6684-4246-3.ch005
El sistema intercomunicacional: de los medios al cambio social mundial
Torres, E. (2023) · Perspectivas de la comunicación · doi:10.56754/0718-4867.2023.3040
ข่าวปลอมทางการเมืองบนเฟซบุ๊กในช่วงการเลือกตั้งทั่วไป พ.ศ. 2562
วงษ์น้อม, ว., & รามสูต, พ. (2019) · Chula Digiverse · doi:10.58837/chula.the.2019.880
Lin, Z., & Wang, Y. (2023) · International Association of Societies of Design Research Congress (IASDR) · doi:10.21606/iasdr.2023.535
The Narrow-Taste Effect: When Consumers Display Narrow Tastes to Algorithmic RecommendersPreprint
Gai, P. J., Leung, E., & Klesse, A.-K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4585195
Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K. (2023) · Research Square · doi:10.21203/rs.3.rs-3626164/v1
ALGORITMOS INDOMÁVEIS: UMA ANÁLISE SEMIOLINGUÍSTICA DA RELAÇÃO ENTRE FÃS E UMA PLATAFORMA DIGITAL
Chimite, Y. F., & Fischer, G. D. (2024) · Revista Prâksis · doi:10.25112/rpr.v1.3608
Mapping the COVID-19 Anti‑Vaccination Communities on Facebook in Czechia
Kaňková, J., & Boomgaarden, H. G. (2024) · Central European Journal of Communication · doi:10.51480/1899-5101.16.2(34).548
Parisi, L. (2024) · SOCIOLOGIA DELLA COMUNICAZIONE · doi:10.3280/sc2023-066006
Storms, E., & Alvarado, O. (2024) · Amsterdam University Press eBooks · doi:10.1515/9789048556908-004 · Cited by 2
HABERE ÇEVRİM İÇİ ERİŞİMDE YENİ DÖNEM: KİŞİSELLEŞTİRİLMİŞ HABER UYGULAMALARI
Çiçeklioğlu, A. Ş. (2024) · Kritik İletişim Çalışmaları Dergisi · doi:10.53281/kritik.1438306
Die Selektion und Organisation von Informationen im Internet
Wörz, F. (2017) · merz | medien + erziehung · doi:10.21240/merz/2017.2.11
Unpacking the Effects of Internet and Algorithmic Factors on Algorithm EngagementPreprint
Zhang, Y., & Zhang, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5060105
A mixed methods exploration of fairness issues in algorithmic policing systems
Udoh, E. S. (2022) · Scholars Archive - University at Albany (University at Albany, State University of New York) · doi:10.54014/tjt0-3vps
Algorithm literacy of Polish students of selected faculties in social sciences and humanities
Iwasiński, Ł., & Krawczyk, L. (2024) · Kultura-Społeczeństwo-Edukacja · doi:10.14746/kse.2024.26.2.15
Totz, C. (2025) · Springer eBooks · doi:10.1007/978-3-658-46355-7_2
Simulacrum in algorithmic social media feeds: perception of truth in the age of artificial visuality
Peleshok, O. (2025) · Synopsis Text Context Media · doi:10.28925/2311-259x.2025.4.10
Subjektive Alltagstheorien und die Aneignung von KI-bezogenen Kompetenzen im Erwachsenenalter
Sūna, L. (2026) · Medien, Kultur, Kommunikation · doi:10.1007/978-3-658-50216-4_13
(2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83549-238-320251010
UĞURHAN, Y. Z. C., & YAŞAR, İ. H. (2026) · Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi · doi:10.19145/e-gifder.1865369
AI and the Hyper-Personalization of News Content
Eder, M. (2026) · Future of business and finance · doi:10.1007/978-3-032-07605-2_21
Psychological Agentic AI: Anchoring Authenticity on Algorithmic Needs
Afolabi, F. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798361
Filtering the Invisible: A Feminist HCI Perspective on Informal Infra-structuring in Gig Labor
Zhao, Z. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791496
“This advances work on algorithmic folk theories by showing how communities coordinate small data experiments to reduce uncertainty and inform practice [11, 27, 42].”
Effects of Adding Friction to Attention-Capture Patterns in TikTok
Alin, J. A., Eck, T. E., & Suoranta, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791716
Breakdowns and Design Opportunities for Collaborative File Management
Seo, K., Lee, Y., Jeong, Y. J., & Gweon, G. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791744
“Prior studies have demonstrated the feasibility of content-and metadata-based automation techniques, showing that they can cluster related documents [10], identify stale files that no longer align with ongoing work [30], and reduce redundant structures in large repositories [40].”
Shim, H., & Eun-Yi, K. (2026) · Asian Communication Research · doi:10.20879/acr.2026.23.006
I Value It, but I Don’t Use It: Attitudes Toward Fact-Checking Among Portuguese University Students
Baptista, J. P., Conrado, F., & Rodrigues, P. C. (2026) · Journalism and Media · doi:10.3390/journalmedia7020089
Beyond disruption and invisibility: Interactional continuity in everyday AI use in India
Edwards, E., & Murthy, D. (2026) · New Media & Society · doi:10.1177/14614448261448545
Karşılıklı Evcilleştirme Bağlamında Spotify Kullanıcılarının Platform Deneyimleri
Şahoğlu, C. T., ÖNAL, A., & ÜZER, B. (2026) · Ankara Üniversitesi ilef dergisi/Ankara üniversitesi ilef dergisi · doi:10.24955/ilef.1842344
Kim, J., & Lu, X. (2026) · The Communication Review · doi:10.1080/10714421.2026.2683213
Cho, J., Rahim, S., Wang, Y., Yu, J., Rheu, M., & Hoefer, M. J. D. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812878
“To address this, mental models have been employed as an analytical lens to understand the causal chains that guide people’s reasoning and decision-making in their use of technology (e.g., [29, 94, 103]).”
Recommending the state: How social media algorithms curate state-created content in China
Lu, Y., Liu, X., & Zhou, C. (2026) · Journal of Communication · doi:10.1093/joc/jqag010 · Cited by 1
Mia, M. S., & Nishat, N. N. (2026) · The International Review of Retail Distribution and Consumer Research · doi:10.1080/09593969.2026.2694426
Li, M. (2026) · Social Media + Society · doi:10.1177/20563051261458615
“One influential approach views algorithms as interactive artifacts and frames public understanding as a cognitive process centered on formal knowledge and skills, often described as algorithmic literacy (Dogruel et al., 2022; Nguyen & Beijnon, 2024; Rader & Gray, 2015).”
Yu, T. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2702139
Examining the Moderating Roles of Algorithm Literacy in eHealth Outcomes Across Age Groups
Bao, H., Wang, L., Lee, E. W. J., Zhi, P., Lin, F., & Wang, X. (2026) · Health Communication · doi:10.1080/10410236.2026.2703683
付, 曼. (2026) · Global Journal of Media Studies · doi:10.26599/gjms.2026.9330021
Ma, L., Wang, Y., & Ma, L. (2026) · Chinese Journal of Communication · doi:10.1080/17544750.2026.2706130
Wang, Y., & Lin, H. (2026) · Global Media and China · doi:10.1177/20594364261474424
Jung, H.-Y. (2026) · Journal of Digital Contents Society · doi:10.9728/dcs.2026.27.7.1871
Liu, D., & Fan, G. F. (2026) · Social Media + Society · doi:10.1177/20563051261475922
“Yet users (including creators) often hold partial or inaccurate mental models of how curation works (Rader & Gray, 2015), and they evaluate platforms not only by outcomes but also by whether decision procedures feel fair, comprehensible, and respectful (Sunshine & Tyler, 2003; Tyler, 2006b).”
A Systematic Bibliometric Review of Artificial Intelligence and Information Literacy Research
Κουτσουπιάς, Ν., & Nosios, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7262-4.ch001
Algorithmisierte Medienumgebungen
Kümpel, A. S., & Fischer, M. (2026) · Springer eBooks · doi:10.1007/978-3-658-46851-4_33-1
Uluk, M. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1391
Li, Y., Zhang, Y., Zhu, C., Han, Y., Gao, X., Yue, J., & Wang, Y. (2026) · Health Informatics Journal · doi:10.1177/14604582261485153
“30 In glioma treatment communication, this model may help professional medical creators reach wider audiences quickly.”
Ala-Luopa, S., Jarrahi, M. H., & Olsson, T. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816898
Retheorizing Context Collapse for Social Media
Chancellor, S., Kaur, H., Terveen, L., & Milton, A. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816991
“In this work, algorithms were framed by users as invisible or opaque, users did not understand why they missed posts from their friends, and content was perceived to be out of sequence [52, 119].”
Compliance Won't Save Trust. What Seven Years of AI and Privacy Research Taught Us
Warmedinger, C. P., & Berrospi, Y. L. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3821402.3830179
A Cross-Country Exploration of Young Consumers’ Perceptions of AI Algorithms in Social Media
Ngelambong, A., Kibat, S. A., Isa, N. F., Zhang, X., Jasrotia, A., & Suasapha, A. H. (2026) · International Conference on Artificial Intelligence and Data Sciences (AiDAS) · doi:10.1109/aidas71914.2026.11712777
“Rader and Gray [13] stated that users usually develop different intuitive beliefs about Facebook’s algorithmic curation based on everyday experiences rather than technical understanding.”
InfluentialMeng, Y., Liu, B., Gao, Z., Ma, S., & Zhou, H. (2026) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3785651.3831520
Trust in algorithms biased appraisal and boosted affect in online dating
Hu, J.-W. M., & Oh, Y. J. (2026) · Information, Communication & Society · doi:10.1080/1369118x.2026.2743730
Algorithmic Interfaces: Mediating Human-AI Interaction
Shin, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-39278-7_2
Metadata
- Authors
- Rader, E., & Gray, R.
- Affiliations
- Michigan State University (US)
- Venue
- Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2015-04-18
- Pages
- 173-182
- OpenAlex
- W2140553307
- Semantic Scholar
- 24f0e8db461cb8a35f642bf07ab407d546c9a0ab
- Cited by
- 386
- References
- 32
- Citations by year
- 2026: 45 · 2025: 25 · 2024: 46 · 2023: 43 · 2022: 41 · 2021: 50 · 2020: 25 · 2019: 33 · 2018: 36 · 2017: 27 · 2016: 12 · 2015: 3
- FWCI
- 124.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Science Foundation (1217212)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- algorithmic curation, Facebook News Feed, algorithm awareness, causal inference, user behavior, social networking sites, information curation
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