Digital Data Donations: Methodologic Approaches for Unearthing the Invisible Within Social Media Algorithmic Systems

This Case Study draws on the ALGOFEED research project, a research initiative investigating how algorithmic recommendation systems concur to affect online content consumption. The project examined algorithmic feedback loops between users and platforms, involving 100 participants aged 18–40 years who donated their social media data. While the project employed a mixed-methods approach combining surveys, data donation, and qualitative interviews, this Case Study focuses specifically on the methodologic dimension of data-donation procedures. We illustrate how researchers can implement data-donation protocols, highlighting the theoretical and epistemological foundations and the best practices surrounding digital data donation. Data donations can enhance participants’ control over their personal data and serve as a methodologic tool to uncover potentially exclusionary patterns obscured by the politics of visibility that govern platforms and their recommender systems. We argue that data donation shifts power dynamics away from platforms, offering researchers an ethical alternative to reveal hidden algorithmic patterns that may disproportionately affect underrepresented or marginalized groups. This Case Study will be useful for researchers doing digital methods in a post-API era as well as for those interested in platform power, digital inequalities, digital content consumption, and algorithmic cultures.