This work focuses on the nature of visibility in societies where the behaviours of humans and algorithms influence each other - termed algorithmically infused societies. We propose a quantitative measure of visibility, with implications and applications to an array of disciplines including communication studies, political science, marketing, technology design, and social media analytics. The measure captures the basic characteristics of the visibility of a given topic, in algorithm/AI-mediated communication/social media settings. Topics, when trending, are ranked against each other, and the proposed measure combines the following two attributes of a topic: (i) the amount of time a topic spends at different ranks, and (ii) the different ranks the topic attains. The proposed measure incorporates a tunable parameter, termed the discrimination level, whose value determines the relative weights of the two attributes that contribute to visibility. Analysis of a large-scale, real-time dataset of trending topics, from one of the largest social media platforms, demonstrates that the proposed measure can explain a large share of the variability of the accumulated views of a topic.

翻译：本研究聚焦于人类行为与算法行为相互影响的社会（即算法化社会）中可见性的本质。我们提出了一种可见性的量化度量方法，该方法对传播学、政治学、市场营销、技术设计及社交媒体分析等多个学科具有启示意义与应用价值。该度量方法能够捕捉在算法/人工智能中介的传播/社交媒体环境中，特定话题可见性的基本特征。当话题流行时，它们会相互竞争排名，而本文提出的度量方法综合了话题的以下两个属性：(i) 话题在不同排名位置停留的时间长度，以及(ii) 话题所达到的不同排名位置。该度量方法引入了一个可调参数，称为区分度，其数值决定了构成可见性的两个属性的相对权重。通过对来自全球最大社交媒体平台之一的大规模实时热门话题数据集的分析表明，所提出的度量方法能够解释话题累计浏览量的大部分变异性。