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Exploring the Utility Versus Intrusiveness of Dynamic Audience Selection on Facebook

Authors:

Sindhu Kiranmai Ernala,

Stephanie S. Yang,

Sauvik DasAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2

Article No.: 342, Pages 1 - 30

https://doi.org/10.1145/3476083

Published: 18 October 2021 Publication History

Abstract

In contrast to existing, static audience controls that map poorly onto users' ideal audiences on social networking sites, dynamic audience selection (DAS) controls can make intelligent inferences to help users select their ideal audience given context and content. But does this potential utility outweigh its potential intrusiveness? We surveyed 250 participants to model users' ideal versus their chosen audiences with static controls and found a significant misalignment, suggesting that DAS might provide utility. We then designed a sensitizing prototype that allowed users to select audiences based on personal attributes, content, or context constraints. We evaluated DAS vis-a-vis Facebook's existing audience selection controls through a counterbalanced summative evaluation. We found that DAS's expressiveness, customizability, and scalability made participants feel more confident about the content they shared on Facebook. However, low transparency, distrust in algorithmic inferences, and the emergence of privacy-violating side channels made participants find the prototype unreliable or intrusive. We discuss factors that affected this trade-off between DAS's utility and intrusiveness and synthesize design implications for future audience selection tools.

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https://dl.acm.org/doi/10.1145/3641021
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Exploring the Utility Versus Intrusiveness of Dynamic Audience Selection on Facebook

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 5, Issue CSCW2

CSCW2

October 2021

5376 pages

EISSN:2573-0142

DOI:10.1145/3493286

Editor:
Jeff Nichols
Apple Inc., United States

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Published: 18 October 2021

Published in PACMHCI Volume 5, Issue CSCW2

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Cited By

Su ZKamath STirakitsoontorn PChen Y(2024)The Hidden Burden: Encountering and Managing (Unintended) Stigma in Children with Serious IllnessesProceedings of the ACM on Human-Computer Interaction10.1145/36410218:CSCW1(1-35)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3641021
Lee HYang YVon Davier TForlizzi JDas S(2024)Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy RisksProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642116(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642116
Saha KGupta PMark GKiciman EDe Choudhury M(2024)Observer Effect in Social Media UseProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642078(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642078
Liu BZhang PShu YGuan ZLu TGu HGu N(2022)Building a Personalized Model for Social Media Textual Content CensorshipProceedings of the ACM on Human-Computer Interaction10.1145/35556576:CSCW2(1-31)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555657

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