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

Authors:

Oscar Alvarado,

Vero Vanden Abeele,

Andreas Breiter,

Katrien VerbertAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 4, Issue CSCW2

Article No.: 121, Pages 1 - 24

https://doi.org/10.1145/3415192

Published: 15 October 2020 Publication History

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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Cited By

Hua YNiu SCai JChilton LHeuer HWohn D(2024)Generative AI in User-Generated ContentExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3636315(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3636315
Felaco C(2024)Researching algorithm awareness: methodological approaches to investigate how people perceive, know, and interact with algorithmsMathematical Population Studies10.1080/08898480.2024.238977931:4(267-288)Online publication date: 30-Aug-2024
https://doi.org/10.1080/08898480.2024.2389779
Zhang YLiu J(2024)Falling behind again? Characterizing and assessing older adults' algorithm literacy in interactions with video recommendationsJournal of the Association for Information Science and Technology10.1002/asi.24960Online publication date: 19-Oct-2024
https://doi.org/10.1002/asi.24960
Show More Cited By

Index Terms

Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 4, Issue CSCW2

CSCW

October 2020

2310 pages

EISSN:2573-0142

DOI:10.1145/3430143

Editor:
Jeff Nichols
Apple Inc.

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Copyright © 2020 ACM.

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Publication History

Published: 15 October 2020

Published in PACMHCI Volume 4, Issue CSCW2

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Cited By

Hua YNiu SCai JChilton LHeuer HWohn D(2024)Generative AI in User-Generated ContentExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3636315(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3636315
Felaco C(2024)Researching algorithm awareness: methodological approaches to investigate how people perceive, know, and interact with algorithmsMathematical Population Studies10.1080/08898480.2024.238977931:4(267-288)Online publication date: 30-Aug-2024
https://doi.org/10.1080/08898480.2024.2389779
Zhang YLiu J(2024)Falling behind again? Characterizing and assessing older adults' algorithm literacy in interactions with video recommendationsJournal of the Association for Information Science and Technology10.1002/asi.24960Online publication date: 19-Oct-2024
https://doi.org/10.1002/asi.24960
Brandimarte L(2023)Parental Trust in Automated Detection of Cyberpredators2023 46th MIPRO ICT and Electronics Convention (MIPRO)10.23919/MIPRO57284.2023.10159713(30-35)Online publication date: 22-May-2023
https://doi.org/10.23919/MIPRO57284.2023.10159713
Gai PLeung EKlesse A(2023)The Narrow-Taste Effect: When Consumers Display Narrow Tastes to Algorithmic RecommendersSSRN Electronic Journal10.2139/ssrn.4585195Online publication date: 2023
https://doi.org/10.2139/ssrn.4585195
Wong YJones RDas RJackson P(2023)Conditional trust: Citizens’ council on data-driven media personalisation and public expectations of transparency and accountabilityBig Data & Society10.1177/2053951723118489210:2Online publication date: 18-Aug-2023
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Oeldorf-Hirsch ANeubaum G(2023)What do we know about algorithmic literacy? The status quo and a research agenda for a growing fieldNew Media & Society10.1177/14614448231182662Online publication date: 1-Jul-2023
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Heuer HGlassman E(2023)Accessible Text Tools for People with Cognitive Impairments and Non-Native Readers: Challenges and OpportunitiesProceedings of Mensch und Computer 202310.1145/3603555.3603569(250-266)Online publication date: 3-Sep-2023
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Recki LEsau-Held MLawo DStevens G(2023)AI said, She said - How Users Perceive Consumer Scoring in PracticeProceedings of Mensch und Computer 202310.1145/3603555.3603562(149-160)Online publication date: 3-Sep-2023
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