Co-Exposure Maximization in Online Social Networks
Abstract
Social media has created new ways for citizens to stay informed on societal matters and participate in political discourse. However, with its algorithmically-curated and virally-propagating content, social media has contributed further to the polarization of opinions by reinforcing users' existing viewpoints. An emerging line of research seeks to understand how content-recommendation algorithms can be re-designed to mitigate societal polarization amplified by social-media interactions. In this paper, we study the problem of allocating seed users to opposing campaigns: by drawing on the equal-time rule of political campaigning on traditional media, our goal is to allocate seed users to campaigners with the aim to maximize the expected number of users who are co-exposed to both campaigns.
Cite
Text
Tu et al. "Co-Exposure Maximization in Online Social Networks." Neural Information Processing Systems, 2020.Markdown
[Tu et al. "Co-Exposure Maximization in Online Social Networks." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/tu2020neurips-coexposure/)BibTeX
@inproceedings{tu2020neurips-coexposure,
title = {{Co-Exposure Maximization in Online Social Networks}},
author = {Tu, Sijing and Aslay, Cigdem and Gionis, Aristides},
booktitle = {Neural Information Processing Systems},
year = {2020},
url = {https://mlanthology.org/neurips/2020/tu2020neurips-coexposure/}
}