Content Sharing Design for Social Welfare in Networked Disclosure Game
Feiran Jia, Chenxi Qiu, Sarah Rajtmajer, Anna Squicciarini
Abstract
This work models the costs and benefits of personal information sharing, or self-disclosure, in online social networks as a networked disclosure game. In a networked population where edges represent visibility amongst users, we assume a leader can influence network structure through content promotion, and we seek to optimize social welfare through network design. Our approach considers user interaction non-homogeneously, where pairwise engagement amongst users can involve or not involve sharing personal information. We prove that this problem is NP-hard. As a solution, we develop a Mixed-integer Linear Programming algorithm, which can achieve an exact solution, and also develop a time-efficient heuristic algorithm that can be used at scale. We conduct numerical experiments to demonstrate the properties of the algorithms and map theoretical results to a dataset of posts and comments in 2020 and 2021 in a COVID-related Subreddit community where privacy risks and sharing tradeoffs were particularly pronounced.
BibTeX
@InProceedings{pmlr-v216-jia23b,
title = {Content Sharing Design for Social Welfare in Networked Disclosure Game},
author = {Jia, Feiran and Qiu, Chenxi and Rajtmajer, Sarah and Squicciarini, Anna},
booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence},
pages = {973--983},
year = {2023},
editor = {Evans, Robin J. and Shpitser, Ilya},
volume = {216},
series = {Proceedings of Machine Learning Research},
month = {31 Jul--04 Aug},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v216/jia23b/jia23b.pdf},
url = {https://proceedings.mlr.press/v216/jia23b.html},
abstract = {This work models the costs and benefits of personal information sharing, or self-disclosure, in online social networks as a networked disclosure game. In a networked population where edges represent visibility amongst users, we assume a leader can influence network structure through content promotion, and we seek to optimize social welfare through network design. Our approach considers user interaction non-homogeneously, where pairwise engagement amongst users can involve or not involve sharing personal information. We prove that this problem is NP-hard. As a solution, we develop a Mixed-integer Linear Programming algorithm, which can achieve an exact solution, and also develop a time-efficient heuristic algorithm that can be used at scale. We conduct numerical experiments to demonstrate the properties of the algorithms and map theoretical results to a dataset of posts and comments in 2020 and 2021 in a COVID-related Subreddit community where privacy risks and sharing tradeoffs were particularly pronounced.}
}