TY - GEN
T1 - Toward Context-Aware Privacy Enhancing Technologies for Online Self-Disclosure
AU - Du, Tingting
AU - Kim, Jiyoon
AU - Squicciarini, Anna
AU - Rajtmajer, Sarah
N1 - Publisher Copyright:
© 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2024
Y1 - 2024
N2 - Voluntary sharing of personal information is at the heart of user engagement on social media and central to platforms' business models. From the users' perspective, so-called self-disclosure is closely connected with both privacy risks and social rewards. Prior work has studied contextual influences on self-disclosure, from platform affordances and interface design to user demographics and perceived social capital. Our work takes a mixed-methods approach to understand the contextual information which might be integrated in the development of privacy-enhancing technologies. Through observational study of several Reddit communities, we explore the ways in which topic of discussion, group norms, peer effects, and audience size are correlated with personal information sharing. We then build and test a prototype privacy-enhancing tool that exposes these contextual factors. Our work culminates in a browser extension that automatically detects instances of self-disclosure in Reddit posts at the time of posting and provides additional context to users before they post to support enhanced privacy decision-making. We share this prototype with social media users, solicit their feedback, and outline a path forward for privacy-enhancing technologies in this space.
AB - Voluntary sharing of personal information is at the heart of user engagement on social media and central to platforms' business models. From the users' perspective, so-called self-disclosure is closely connected with both privacy risks and social rewards. Prior work has studied contextual influences on self-disclosure, from platform affordances and interface design to user demographics and perceived social capital. Our work takes a mixed-methods approach to understand the contextual information which might be integrated in the development of privacy-enhancing technologies. Through observational study of several Reddit communities, we explore the ways in which topic of discussion, group norms, peer effects, and audience size are correlated with personal information sharing. We then build and test a prototype privacy-enhancing tool that exposes these contextual factors. Our work culminates in a browser extension that automatically detects instances of self-disclosure in Reddit posts at the time of posting and provides additional context to users before they post to support enhanced privacy decision-making. We share this prototype with social media users, solicit their feedback, and outline a path forward for privacy-enhancing technologies in this space.
UR - http://www.scopus.com/inward/record.url?scp=85208419970&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85208419970&partnerID=8YFLogxK
U2 - 10.1609/hcomp.v12i1.31599
DO - 10.1609/hcomp.v12i1.31599
M3 - Conference contribution
AN - SCOPUS:85208419970
SN - 9781577358930
T3 - Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, HCOMP
SP - 44
EP - 54
BT - HCOMP 2024 - 12th AAAI Conference on Human Computation and Crowdsourcing
A2 - Demartini, Gianluca
A2 - Gadiraju, Ujwal
PB - Association for the Advancement of Artificial Intelligence
T2 - 12th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2024
Y2 - 16 October 2024 through 19 October 2024
ER -