This article evaluates the quality of data collection in individual-level desktop web tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard for the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic web tracking solution, WebTrack, an open-source tracking tool maintained by a major European research institution, GESIS. The design logic, the interfaces, and the backend requirements for WebTrack are discussed, followed by a detailed examination of the strengths and weaknesses of the tool. Finally, using data from 1,185 participants, the article empirically illustrates how an improvement in data collection through WebTrack leads to innovative shifts in the use of tracking data. As WebTrack allows for collecting the content people are exposed to beyond the classical news platforms, it can greatly improve the detection of politics-related information consumption in tracking data through automated content analysis compared to traditional approaches that rely on the source-level analysis.

中文翻译：

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提高个人级 Web 跟踪的质量：现有方法的挑战以及新内容和长尾敏感学术解决方案的引入


本文评估了社会科学中使用的个人级桌面 Web 跟踪中的数据收集质量，并表明现有方法面临采样问题、由于缺乏内容级数据以及无视各种设备和长尾消费模式而导致的有效性问题，以及透明度和隐私问题。为了克服其中的一些问题，本文介绍了一种新的学术 Web 跟踪解决方案 WebTrack，这是一种由欧洲主要研究机构 GESIS 维护的开源跟踪工具。讨论了 WebTrack 的设计逻辑、接口和后端要求，然后详细研究了该工具的优缺点。最后，使用来自 1,185 名参与者的数据，本文实证说明了通过 WebTrack 收集数据的改进如何导致跟踪数据使用的创新转变。由于 WebTrack 允许收集人们在传统新闻平台之外接触到的内容，因此与依赖于源级分析的传统方法相比，它可以通过自动内容分析大大提高跟踪数据中对政治相关信息消耗的检测。