算法注入可见性的一种动态测度
A dynamical measure of algorithmically infused visibility
AI总结：
针对算法注入社会中的社交媒体可见性问题，提出结合主题排名停留时间与所达排名、并由区分水平参数调节权重的动态测度，实验表明其可有效解释主题累计浏览量变异。
摘要：
本工作聚焦于人类行为与算法行为相互影响的社会——即所谓算法注入社会——中可见性的本质。我们提出一种可见性的定量测度，其对传播学、政治学、营销学、技术设计和社交媒体分析等一系列学科均具有启示和应用价值。该测度刻画在算法/AI中介的传播/社交媒体环境中，给定主题可见性的基本特征。当主题处于热门趋势时，它们会被相互排名，而所提出的测度结合了一个主题的以下两个属性：（i）该主题在不同排名上停留的时间量，以及（ii）该主题达到的不同排名。所提出的测度纳入了一个可调参数，称为区分水平，其取值决定了促成可见性的两个属性的相对权重。对来自最大社交媒体平台之一的大规模实时热门主题数据集的分析表明，所提出的测度能够解释一个主题累计浏览量变异的很大一部分。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
