Maximizing the Diversity of Exposure in a Social Network

TL;DR

This paper introduces a novel approach to enhance viewpoint diversity in social networks by recommending news articles to users, balancing information spread with exposure to diverse opinions, using scalable algorithms based on influence maximization principles.

Contribution

It formulates a new influence maximization problem for diversity of exposure, incorporating content and user leanings, and develops scalable approximation algorithms with a novel extension of reverse-reachable sets.

Findings

Algorithms are scalable and efficient on real-world datasets.

The approach effectively balances information spread and viewpoint diversity.

Experimental results demonstrate improved diversity exposure in social networks.

Abstract

Social-media platforms have created new ways for citizens to stay informed and participate in public debates. However, to enable a healthy environment for information sharing, social deliberation, and opinion formation, citizens need to be exposed to sufficiently diverse viewpoints that challenge their assumptions, instead of being trapped inside filter bubbles. In this paper, we take a step in this direction and propose a novel approach to maximize the diversity of exposure in a social network. We formulate the problem in the context of information propagation, as a task of recommending a small number of news articles to selected users. We propose a realistic setting where we take into account content and user leanings, and the probability of further sharing an article. This setting allows us to capture the balance between maximizing the spread of information and ensuring the exposure of users to diverse viewpoints. The resulting problem can be cast as maximizing a monotone and submodular function subject to a matroid constraint on the allocation of articles to users. It is a challenging generalization of the influence maximization problem. Yet, we are able to devise scalable approximation algorithms by introducing a novel extension to the notion of random reverse-reachable sets. We experimentally demonstrate the efficiency and scalability of our algorithm on several real-world datasets.