Balancing Information Exposure in Social Networks

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.

💡 Research Summary

The paper tackles the problem of balancing information exposure in social networks where two opposing campaigns (or viewpoints) spread simultaneously. Unlike traditional competitive influence maximization, which treats each campaign as a selfish player, the authors assume a centralized authority that wants to select additional seed nodes for both campaigns so that the number of “balanced” users is maximized. A balanced user is defined as one who either receives information from both campaigns or from none, i.e., the symmetric difference between the two reach sets is minimized.

Formally, given a directed graph G = (V, E) with two edge‑wise propagation probabilities p₁(e) and p₂(e), initial seed sets I₁ and I₂, and a budget k, the task is to choose two sets S₁ and S₂ (|S₁| + |S₂| ≤ k) that maximize

Φ(S₁,S₂) = E