Data mirroring: A methodological framework for data-donation-based interviews in media use research

Abstract

This article introduces “data mirroring,” a methodological framework for conducting data-donation-based interviews using Data Download Packages (DDPs) from digital platforms. Since the General Data Protection Regulation took effect, DDPs have found application in research. While the literature on the value of DDPs primarily points toward scaling and validating aggregate-level data, their potential to illuminate complex user–media relationships within datafied environments at the micro-level appears underexplored. Drawing from recent conceptualizations of the “data mirror,” which captures the feedback loops between users and digital media, this article provides theoretical grounding and practical guidelines for “mirroring” DDPs to users. Based on exercises with 64 participants, we articulate through an illustrative case study how DDPs can serve as prompts, contexts, and reflections, revealing reflexive strategies users employ to curate information flows on algorithmic platforms like Instagram. In addition, we introduce an open-source web application to operationalize DDPs as data mirrors for non-technical researchers.