The goal of this project is to address concerns about the privacy risks to personal information sharing in social media. Low socioeconomic and underrepresented populations report feeling especially concerned about their digital privacy in social media. The project team is studying how socioeconomic status and group identity influence the challenges of privacy self-management. New datasets collected and labeled through this award enable new machine learning approaches for detection of self-disclosed personal information, social network and peer effects on privacy-relevant behaviors, and population-specific privacy risks. Privacy-enhancing technologies designed and developed during the project will support privacy self-management for social media users. To accomplish the goals of the project, research is being undertaken to understand differences in self-disclosure attitudes and behaviors across socioeconomic groups. To do so, the project is developing and integrating novel measures of social media self-relevant sharing behavior, including audience, sensitivity of disclosure, direct vs. indirect disclosure, group privacy, and platform and community norms. The research team is: conducting small- and big-data empirical studies of personal disclosure, developing data-informed descriptions of normative information flow and metrics for measurement of contextual integrity, and is experimenting with prototype technologies to bring equity to privacy management. The project is based on core principles of participatory design and governance. These principles are operationalized through broad community outreach and embedded processes for mutual learning, including field studies, community workshops, and cooperative prototyping.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|Effective start/end date||6/1/23 → 5/31/26|
- National Science Foundation: $599,851.00
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