Abstract
Social media platforms like Facebook utilize AI algorithms to personalize content based on user data, raising concerns about data privacy and transparency. We introduce the Facebook Data Shield (FDS), a life-sized interactive installation that empowers users to visualize and control the data shared with the platform. We deployed FDS at a public design event, to explore user data-sharing and control preferences. We conducteded an analysis of 81 user interactions, based on data logs and surveys. Our findings reveal a preference for increased data control, particularly concerning online behavior and demographics. We identify five distinct clusters for preferred data-sharing settings, which show limited correlation with demographic information. Finally, we discuss the potential for predicting preferred data-sharing settings through machine learning based on our data, and implications for social media platform design. This study contributes to the ongoing discourse on data governance and user autonomy in an era of AI-driven content curation.
Keywords
user study; data sharing; social media; tangible interaction
DOI
https://doi.org/10.21606/drs.2024.739
Citation
Jansen, A., Sinsel, J., and Colombo, S. (2024) Facebook Data Shield: An interactive tangible interface for user data control, in Gray, C., Ciliotta Chehade, E., Hekkert, P., Forlano, L., Ciuccarelli, P., Lloyd, P. (eds.), DRS2024: Boston, 23–28 June, Boston, USA. https://doi.org/10.21606/drs.2024.739
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Research Paper
Included in
Facebook Data Shield: An interactive tangible interface for user data control
Social media platforms like Facebook utilize AI algorithms to personalize content based on user data, raising concerns about data privacy and transparency. We introduce the Facebook Data Shield (FDS), a life-sized interactive installation that empowers users to visualize and control the data shared with the platform. We deployed FDS at a public design event, to explore user data-sharing and control preferences. We conducteded an analysis of 81 user interactions, based on data logs and surveys. Our findings reveal a preference for increased data control, particularly concerning online behavior and demographics. We identify five distinct clusters for preferred data-sharing settings, which show limited correlation with demographic information. Finally, we discuss the potential for predicting preferred data-sharing settings through machine learning based on our data, and implications for social media platform design. This study contributes to the ongoing discourse on data governance and user autonomy in an era of AI-driven content curation.