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Cited by 14Privacy, Security, and Data ProtectionInnovative Human-Technology InteractionUser Authentication and Security Systems

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To Self-Persuade or be Persuaded: Examining Interventions for Users’ Privacy Setting Selection

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Krsek, Isadora, Wenzel, Kimi, Das, Sauvik, Hong, Jason I., Dabbish, Laura·2022·CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3491102.3502009
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Abstract

User adoption of security and privacy (S&P) best practices remains low, despite sustained efforts by researchers and practitioners. Social influence is a proven method for guiding user S&P behavior, though most work has focused on studying peer influence, which is only possible with a known social graph. In a study of 104 Facebook users, we instead demonstrate that crowdsourced S&P suggestions are significantly influential. We also tested how reflective writing affected participants’ S&P decisions, with and without suggestions. With reflective writing, participants were less likely to accept suggestions — both social and Facebook default suggestions. Of particular note, when reflective writing participants were shown the Facebook default suggestion, they not only rejected it but also (unknowingly) configured their settings in accordance with expert recommendations. Our work suggests that both non-personal social influence and reflective writing can positively influence users’ S&P decisions, but have negative interactions.

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  • The premise of a successful user community is the critical mass of user-generated high-quality content.

    “The premise of a successful user community is the critical mass of user-generated high-quality content [ 40 ].”

    From "It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools (2024) · cites this paper as [40]

    The Introduction and §2.2 discuss social influence, Facebook participation, and crowdsourced privacy-setting advice, but do not address whether a successful user community requires a critical mass of high-quality user-generated content. Searches for that phrase and for community success, content quality, and participation found no supporting passage in our copy. (LLM, conf=0.76)

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Research works printed but not linked: 87

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Metadata

Authors
Krsek, I., Wenzel, K., Das, S., Hong, J. I., & Dabbish, L.
Affiliations
  • Carnegie Mellon University (US)
  • Georgia Institute of Technology (US)
ORCID
Venue
CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2022-04-29
Pages
1-17
DOI
10.1145/3491102.3502009
OpenAlex
W4225155293
Cited by
14
References
63
Citations by year
2026: 2 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 1
FWCI
9.32
Type
conference paper
Language
en
Open access
Licence
Locations
CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
Funders
  • National Science Foundation (1704087)
  • Facebook (1212897202397420)
Topics
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
Keywords
privacy settings, social influence, reflective writing, Facebook, default privacy settings, expert recommendations, self-persuasion

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