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.
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“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
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Cited by
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Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
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“As Rader and Gray (2015) point out, the feedback-loop characteristics of these systems make user beliefs an important component in shaping the overall system behaviour.”
BackgroundInfluentialLee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338
“First, our research contributes to emerging studies that investigate people’s mental models and folk theories of algorithms (Bucher, 2016; Eslami et al., 2015; Rader and Gray, 2015), and social studies on automation, artificial intelligence and machine learning more broadly.”
BackgroundInfluentialA Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Mohseni, S., Zarei, N., & Ragan, E. D. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387166 · Cited by 785
“In order to find out how an intelligent system should explain its behavior for non-experts, research on machine learning explanations has studied how users interpret intelligent agents [47, 164] and algorithms [171] to find out what users expect from machine explanations.”
BackgroundImproving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300830 · Cited by 777
“…influence every facet of our lives, including the quality of healthcare and education we receive [16, 31, 32, 49], which news or social media posts we see [4, 18, 84], who receives a job [46, 82], who is released from jail [8, 23], and who is subjected to increased policing [65, 71, 94].”
Background
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
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