Balancing Information Exposure in Social Networks

TL;DR

This paper addresses the challenge of balancing information exposure in social networks with opposing campaigns, proposing novel algorithms for a complex, non-monotone problem and evaluating their effectiveness.

Contribution

It introduces a new model for balanced information exposure using a symmetric difference function and develops approximation algorithms for this complex problem.

Findings

Proposed algorithms effectively balance information exposure in social networks.

The model handles non-monotone, non-submodular functions, unlike previous approaches.

Experimental results demonstrate the algorithms' practical performance.

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 we offer significant novelties. 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 best response strategies (i.e., move of the last player), we consider a setting with a centralized agent and provide bounds for a global objective function.