  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.
