Digital trace data collection through data donation

TL;DR

This paper explores the use of GDPR-mandated data download packages (DDPs) for social science research, providing a blueprint and error framework to ensure data quality and representativeness in this new data collection method.

Contribution

It introduces a comprehensive blueprint and a total error framework for collecting and analyzing digital trace data via DDPs, addressing methodological challenges and quality control.

Findings

Proposes a systematic error framework for DDP-based data collection.

Provides a practical quality control checklist for researchers.

Highlights the potential of DDPs for large-scale social science research.

Abstract

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.