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Photo Privacy Conflicts in Social Media: A Large-scale Empirical Study

Authors:

Sören Preibusch,

Adam JoinsonAuthors Info & Claims

CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

Pages 3821 - 3832

https://doi.org/10.1145/3025453.3025668

Published: 02 May 2017 Publication History

Abstract

Items in social media such as photos may be co-owned by multiple users, i.e., the sharing decisions of the ones who upload them have the potential to harm the privacy of the others. Previous works uncovered coping strategies by co-owners to manage their privacy, but mainly focused on general practices and experiences. We establish an empirical base for the prevalence, context and severity of privacy conflicts over co-owned photos. To this aim, a parallel survey of pre-screened 496 uploaders and 537 co-owners collected occurrences and type of conflicts over co-owned photos, and any actions taken towards resolving them. We uncover nuances and complexities not known before, including co-ownership types, and divergences in the assessment of photo audiences. We also find that an all-or-nothing approach seems to dominate conflict resolution, even when parties actually interact and talk about the conflict. Finally, we derive key insights for designing systems to mitigate these divergences and facilitate consensus.

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Xu AZhou ZMiyazaki KYoshikawa RHosio SYatani K(2024)DIPA2Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314397:4(1-30)Online publication date: 12-Jan-2024
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Index Terms

Photo Privacy Conflicts in Social Media: A Large-scale Empirical Study
1. Human-centered computing
2. Social and professional topics
  1. Computing / technology policy

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cover image ACM Conferences

CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

May 2017

7138 pages

ISBN:9781450346559

DOI:10.1145/3025453

General Chairs:
Gloria Mark
University of California Irvine
,
Susan Fussell
Cornell University
,
Program Chairs:
Cliff Lampe
University of Michigan
,
m.c. schraefel
University of Southampton
,
Juan Pablo Hourcade
University of Iowa
,
Caroline Appert
Université Paris-Sud
,
Daniel Wigdor
University of Toronto

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Published: 02 May 2017

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Engineering and Physical Sciences Research Council

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CHI '17

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CHI '17: CHI Conference on Human Factors in Computing Systems

May 6 - 11, 2017

Colorado, Denver, USA

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CHI '17 Paper Acceptance Rate 600 of 2,400 submissions, 25%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Zhan XSeymour WSuch J(2024)Beyond Individual Concerns: Multi-user Privacy in Large Language ModelsProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665883(1-6)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665883
Zhan XAbdi NSeymour WSuch J(2024)Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to UseProceedings of the ACM on Human-Computer Interaction10.1145/36373398:CSCW1(1-37)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637339
Xu AZhou ZMiyazaki KYoshikawa RHosio SYatani K(2024)DIPA2Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314397:4(1-30)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631439
Zhang LStangl ASharma TTseng YXu IGurari DWang YFindlater L(2024)Designing Accessible Obfuscation Support for Blind Individuals’ Visual Privacy ManagementProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642713(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642713
Velykoivanenko LSalehzadeh Niksirat KTeofanovic SChapuis BMazurek MHuguenin K(2024)Designing a Data-Driven Survey System: Leveraging Participants' Online Data to Personalize SurveysProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642572(1-22)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642572
Xu AFang SYang HHosio SYatani K(2024)Examining Human Perception of Generative Content Replacement in Image Privacy ProtectionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642103(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642103
Di Scala DYolum PAgmon NAn BRicci AYeoh W(2023)PACCART: Reinforcing Trust in Multiuser Privacy Agreement SystemsProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599078(2787-2789)Online publication date: 30-May-2023
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Mondal MPunuru ACheng TVargas KGundry CDriggs NSchill NCarlson NBedwell JLorenc JGhosh ILi YFulda NPage X(2023)A Tale of Two Cultures: Comparing Interpersonal Information Disclosure Norms on TwitterProceedings of the ACM on Human-Computer Interaction10.1145/36100457:CSCW2(1-40)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610045
Nourmohammadzadeh Motlagh FAlhosseini SCheng FMeinel C(2023)An Approach to Multi-Party Privacy Conflict Resolution for Co-owned Images on Content Sharing PlatformsProceedings of the 2023 8th International Conference on Machine Learning Technologies10.1145/3589883.3589923(264-268)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.1145/3589883.3589923
Xu AZhou ZMiyazaki KYoshikawa RHosio SYatani K(2023)DIPA : An Image Dataset with Cross-cultural Privacy Concern AnnotationsCompanion Proceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581754.3584176(259-266)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581754.3584176
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