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Cited by 70Recommender Systems and TechniquesEthics and Social Impacts of AIInnovative Human-Technology Interaction

Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube

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Alvarado, Oscar, Heuer, Hendrik, Vanden Abeele, Vero, Breiter, Andreas, Verbert, Katrien·2020·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3415192
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Abstract

User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these beliefs is crucial because they influence how users interact with recommendation algorithms. With no prior work on user beliefs of algorithmic video recommendations, practitioners lack relevant knowledge to improve the user experience of such systems. To address this problem, we conducted semi-structured interviews with middle-aged YouTube video consumers to analyze their user beliefs about the video recommendation system. Our analysis revealed different factors that users believe influence their recommendations. Based on these factors, we identified four groups of user beliefs: Previous Actions, Social Media, Recommender System, and Company Policy. Additionally, we propose a framework to distinguish the four main actors that users believe influence their video recommendations: the current user, other users, the algorithm, and the organization. This framework provides a new lens to explore design suggestions based on the agency of these four actors. It also exposes a novel aspect previously unexplored: the effect of corporate decisions on the interaction with algorithmic recommendations. While we found that users are aware of the existence of the recommendation system on YouTube, we show that their understanding of this system is limited.

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  • Algorithmic distribution ties in with other studies of user perception and folk theories about curation algorithms, where users puzzle over distribution of content.

    “Algorithmic distribution ties in with other studies of user perception and folk theories about curation algorithms (Alvarado et al. 2020; DeVito et al. 2017; Eslami et al. 2016), where users puzzle over the distribution of content.”

    From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Alvarado et al. 2020

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Authors
Alvarado, O., Heuer, H., Vanden Abeele, V., Breiter, A., & Verbert, K.
Affiliations
  • KU Leuven (BE)
  • University of Bremen (DE)
  • KU Leuven, Leuven, Belgium
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2020-10-14
Volume
4
Issue
CSCW2
Pages
1-24
ISSN
2573-0142 (ISSN-L)
DOI
10.1145/3415192
OpenAlex
W3047749867
Cited by
70
References
6
Citations by year
2026: 17 · 2025: 9 · 2024: 10 · 2023: 17 · 2022: 13 · 2021: 4
FWCI
7.99
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Lirias (submittedVersion, other-oa)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • DFG (374666841)
  • KU Leuven Research Council (24/16/017)
  • University of Costa Rica
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
algorithmic recommendation, user beliefs, YouTube, social media, business policy, user agency, recommender system design

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