论文标题
网络和算法中的少数群体
Minorities in networks and algorithms
论文作者
论文摘要
在本章中，我们概述了数据驱动和理论的社交网络复杂模型及其在理解社会不平等和边缘化方面的潜力。我们专注于网络和基于网络的算法以及它们如何影响少数群体引起的不平等。特别是，我们研究了同质和混合偏见如何塑造大型和小型社交网络，影响少数民族的看法并影响协作模式。我们还讨论了网络和网络形成和健康不平等的动态过程。此外，我们认为网络建模是揭示排名和社会推荐算法对少数群体可见性的影响至关重要的。最后，我们强调了这个新兴研究主题中的主要挑战和未来机会。
In this chapter, we provide an overview of recent advances in data-driven and theory-informed complex models of social networks and their potential in understanding societal inequalities and marginalization. We focus on inequalities arising from networks and network-based algorithms and how they affect minorities. In particular, we examine how homophily and mixing biases shape large and small social networks, influence perception of minorities, and affect collaboration patterns. We also discuss dynamical processes on and of networks and the formation of norms and health inequalities. Additionally, we argue that network modeling is paramount for unveiling the effect of ranking and social recommendation algorithms on the visibility of minorities. Finally, we highlight the key challenges and future opportunities in this emerging research topic.
