Maximizing the Diversity of Exposure in a Social Network

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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 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 take into account content and user leanings, and the probability of further sharing an article. Our model 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.

Original languageEnglish
JournalIEEE Transactions on Knowledge and Data Engineering
Publication statusE-pub ahead of print - 2020
MoE publication typeA1 Journal article-refereed


  • Approximation algorithms
  • Computer science
  • Cultural differences
  • Greedy algorithms
  • Resource management
  • Social networking (online)
  • Task analysis

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