How do people consume news online? Here, we propose a novel way to answer this question using the browsing behavior of web users and the networks they form while navigating news content. In these networks, two news outlets are connected if they share a fraction of their audiences. We propose two crucial improvements to the methodology employed in previous research: a statistical test to filter out non-significant overlap between sites; and a thresholding approach to identify the core of the audience network. We explain why our approach is better than previous approaches using two data sets: one tracks digital news consumption during the 2016 Brexit referendum in the UK and the other during the 2016 Presidential Election in the US. We show that our filtering technique produces a completely different ranking of top sites, uncovering structural properties in the audience network that would go unnoticed otherwise.

中文翻译：

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数字新闻消费中的受众网络重叠

人们如何在网上消费新闻？在这里，我们提出了一种使用网络用户的浏览行为和他们在浏览新闻内容时形成的网络来回答这个问题的新方法。在这些网络中，如果两家新闻媒体共享一小部分受众，它们就会相互关联。我们对先前研究中采用的方法提出了两项​​重要改进：用于过滤站点之间不显着重叠的统计测试；以及确定受众网络核心的阈值方法。我们使用两个数据集解释了为什么我们的方法比以前的方法更好：一个跟踪 2016 年英国脱欧公投期间的数字新闻消费，另一个跟踪美国 2016 年总统大选期间的数字新闻消费。我们表明我们的过滤技术产生了完全不同的顶级网站排名，