""It Doesn't Just Feel Like Something a Lawyer Slapped Together." Menta" by Rizu Paudel, Ankit Shrestha et al.
 

Document Type

Conference Paper

Journal/Book Title/Conference

2023 Conference on Computer Supported Cooperative Work and Social Computing

Publisher

Association for Computing Machinery

Publication Date

10-14-2023

Journal Article Version

Version of Record

First Page

298

Last Page

306

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

Users are often unaware of the information that applications collect and are surprised by unexpected data collection and sharing practices. With numerous third-party applications on Facebook potentially accessing the personal information of billions of users, it is essential to understand users’ mental models of data sharing to help them make informed decisions. To achieve this, we conducted semi-structured interviews using drawings and think-aloud protocol with 32 participants. Our participants had misconceptions regarding third-party applications’ data sharing practices with varied mental models. Based on these findings, we created mental model-based privacy policy design that prompts users to consider a specific scenario and provides information to help them understand their misconceptions. To evaluate our designs, we then conducted an online study with 26 participants over Amazon Mechanical Turk (MTurk). Our results showed that using mental models helped users comprehend the message in the privacy policy, connect them to the design, and grabbed their attention. Finally, we offer recommendations for future research regarding the usage of mental models in designs to combat users’ misconceptions with an effortless depiction of privacy policy.

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