Co-exposure Maximization in Online Social Networks
Sijing Tu, Cigdem Aslay, Aristides Gionis
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.
BibTeX
@inproceedings{NEURIPS2020_212ab20d,
author = {Tu, Sijing and Aslay, Cigdem and Gionis, Aristides},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {3232--3243},
publisher = {Curran Associates, Inc.},
title = {Co-exposure Maximization in Online Social Networks},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/212ab20dbdf4191cbcdcf015511783f4-Paper.pdf},
volume = {33},
year = {2020}
}