Understanding how people interact with the web is key for a variety of applications, e.g., from the design of effective web pages to the definition of successful online marketing campaigns. Browsing behavior has been traditionally represented and studied by means of clickstreams , i.e., graphs whose vertices are web pages, and edges are the paths followed by users. Obtaining large and representative data to extract clickstreams is, however, challenging. The evolution of the web questions whether browsing behavior is changing and, by consequence, whether properties of clickstreams are changing. This article presents a longitudinal study of clickstreams from 2013 to 2016. We evaluate an anonymized dataset of HTTP traces captured in a large ISP, where thousands of households are connected. We first propose a methodology to identify actual URLs requested by users from the massive set of requests automatically fired by browsers when rendering web pages. Then, we characterize web usage patterns and clickstreams, taking into account both the temporal evolution and the impact of the device used to explore the web. Our analyses precisely quantify various aspects of clickstreams and uncover interesting patterns, such as the typical short paths followed by people while navigating the web, the fast increasing trend in browsing from mobile devices, and the different roles of search engines and social networks in promoting content. Finally, we contribute a dataset of anonymized clickstreams to the community to foster new studies.<sup;>1</sup;>

中文翻译：

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你、网络和你的设备

了解人们如何与网络交互是各种应用程序的关键，例如，从有效网页的设计到成功的在线营销活动的定义。浏览行为传统上通过以下方式表示和研究点击流，即图，其顶点是网页，边是用户所遵循的路径。然而，获取大量且具有代表性的数据以提取点击流具有挑战性。网络的发展质疑浏览行为是否正在发生变化，因此点击流的属性是否正在发生变化。本文介绍了对 2013 年至 2016 年点击流的纵向研究。我们评估了在连接了数千个家庭的大型 ISP 中捕获的 HTTP 跟踪的匿名数据集。我们首先提出了一种方法，用于从浏览器在呈现网页时自动触发的大量请求中识别用户请求的实际 URL。然后，我们描述了网络使用模式和点击流，同时考虑了时间演变和用于探索网络的设备的影响。我们的分析精确量化了点击流的各个方面并揭示了有趣的模式，例如人们在浏览网络时遵循的典型短路径、移动设备浏览的快速增长趋势以及搜索引擎和社交网络在推广内容方面的不同作用. 最后，我们向社区贡献了一个匿名点击流数据集，以促进新的研究。<sup;>1</sup;>