Balancing information exposure in social networks

Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti

Research output: Contribution to journalConference articlepeer-review

27 Scopus citations

Abstract

Social media has brought a revolution on how people are consuming news. Beyond the undoubtedly large number of advantages brought by social-media platforms, a point of criticism has been the creation of echo chambers and filter bubbles, caused by social homophily and algorithmic personalization. In this paper we address the problem of balancing the information exposure in a social network. We assume that two opposing campaigns (or viewpoints) are present in the network, and that network nodes have different preferences towards these campaigns. Our goal is to find two sets of nodes to employ in the respective campaigns, so that the overall information exposure for the two campaigns is balanced. We formally define the problem, characterize its hardness, develop approximation algorithms, and present experimental evaluation results. Our model is inspired by the literature on influence maximization, but there are significant differences from the standard model. First, balance of information exposure is modeled by a symmetric difference function, which is neither monotone nor submodular, and thus, not amenable to existing approaches. Second, while previous papers consider a setting with selfish agents and provide bounds on bestresponse strategies (i.e., move of the last player), we consider a setting with a centralized agent and provide bounds for a global objective function.

Original languageEnglish (US)
Pages (from-to)4664-4672
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2017-December
StatePublished - 2017
Externally publishedYes
Event31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States
Duration: Dec 4 2017Dec 9 2017

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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