通过数据捐赠收集数据的数字跟踪数据 (Digital trace data collection through data donation)

A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU's 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with "data download packages" (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens' digital life can be obtained and analyzed to answer social-scientific questions - with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a "total error framework" for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation.

翻译：欧盟2018年《数据保护总条例》第15条规定,个人可通过电子途径获取个人数据副本,而所有主要数字平台现在都遵守这项法律,向用户提供“数据下载包”(DDPs),因此,通过自愿捐赠DDPs,可以获取和分析公共和私人实体在公民数字生活过程中收集的所有数据,以在征得同意的情况下回答社会科学问题。因此,同意的DDPs为大量新的研究机会开辟了道路。然而,尽管这一全新的数据收集方法无疑将在未来几年中受到欢迎,但它也提出了自己的代表性和测量质量问题,往往通过错误框架对之进行系统评估。因此,在本文件中,我们为利用DDPs收集数字跟踪数据提供了蓝图,并为这类项目设计了一个“完全错误框架”。我们通过数据捐赠收集数字跟踪数据的错误框架是为了便利利用DDPs进行高质量的社会科学研究,同时以批判性的方式反映其独特的方法挑战以及新的调查来源。我们通过这种方法来利用新的方法来控制,提供了一种机会。