March 30, 2026
How Much Data Should I Request? Balancing Richness and Compliance in Digital Trace Data Donations
Asking people to donate more of their social media data can lead to richer results, but may also mean losing more participants along the way. This study shows that Facebook and Instagram data requests spanning longer periods increase dropout largely due to the longer delivery time from the platform, while also fundamentally shaping what researchers can validly measure.
Abstract
Digital trace “data donation” studies offer researchers a unique opportunity to collect high-quality behavioral data, but decisions about the scope of requested data can impact both dataset richness and participant compliance. This paper examines the tradeoffs between requesting larger data packages, which include more extensive historical records, and participants’ willingness to donate. In a randomized experiment with Facebook and Instagram data donations, we compare a control condition where participants are asked to request the default 1-year data period to a treatment condition in which they are asked to request data for their entire account history. We analyze how different request sizes affect (1) participants’ compliance rates and (2) the characteristics of the data resulting from these different requests. We find that participants asked to request more data are less likely to complete the task. However, we propose that this is not primarily due to heightened privacy concerns, but rather because these data packages are significantly larger and therefore take longer for the platforms to deliver. This additional time to deliver data packages results in increased attrition. In terms of the effects on the data itself, we show that decisions about the time-span of the data impacts not only the volume of data requested, but also has implications for measurement validity, as the temporal window fundamentally redefines what key constructs represent, potentially transforming intended static indicators into narrow snapshots of recent behavior. We provide guidance for researchers navigating these decisions, considering both the benefits of richer longitudinal data and the risks of reduced participation.
Authors
Health / sector leader
Prof. dr. Daniel Oberski
Professor of social and health data science / Utrecht University
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Media
Prof. dr. Theo Araujo
Professor of Media, Organisations and Society / University of Amsterdam
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Media / Sector Leader
Prof. dr. Claes de Vreese
Distinguished university professor of artificial intelligence and society with a special focus on media and democracy / University of Amsterdam
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