Social media have a great potential to improve information dissemination in our society, yet they have been held accountable for a number of undesirable effects, such as polarization and filter bubbles. It is thus important to understand these negative phenomena and develop methods to combat them. In this paper, we propose a novel approach to address the problem of breaking filter bubbles in social media. We do so by aiming to maximize the diversity of the information exposed to connected social-media users. We formulate the problem of maximizing the diversity of exposure as a quadratic-knapsack problem. We show that the proposed diversity-maximization problem is inapproximable, and thus, we resort to polynomial nonapproximable algorithms, inspired by solutions developed for the quadratic-knapsack problem, as well as scalable greedy heuristics. We complement our algorithms with instance-specific upper bounds, which are used to provide empirical approximation guarantees for the given problem instances. Our experimental evaluation shows that a proposed greedy algorithm followed by randomized local search is the algorithm of choice given its quality-vs.-efficiency trade-off.

中文翻译：

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告诉我我的朋友不知道的事情：社交网络中的多样性最大化

社交媒体具有改善我们社会中信息传播的巨大潜力，但它们却被要求对许多不良影响负责，例如两极分化和滤泡。因此，重要的是要了解这些负面现象并制定应对措施。在本文中，我们提出了一种新颖的方法来解决社交媒体中打破过滤器气泡的问题。我们这样做的目的是最大程度地提高社交媒体用户所接触信息的多样性。我们将最大化暴露多样性的问题公式化为二次背包问题。我们表明，提出的分集最大化问题是不可近似的，因此，我们采用了针对二次背包问题以及可伸缩贪婪启发式算法开发的多项式不可近似算法。我们用特定于实例的上限来补充我们的算法，该上限用于为给定问题实例提供经验近似保证。我们的实验评估表明，考虑到质量与效率之间的权衡，提出的贪婪算法和随机局部搜索是首选算法。