Post-Digital Data-Gathering and the Adaptive Epistemological Framework: Navigating the Human-Algorithm-Platform Nexus

Abstract

In the post-digital condition, where the digital is no longer a separate domain but the pervasive environment of social life, this article addresses the methodological and epistemological challenges of studying human-algorithm-platform interactions. Digital platforms are understood as socio-technical systems that mediate user behavior through opaque algorithmic mechanisms, producing data as co-constructed artifacts rather than neutral traces. The paper proposes an adaptive epistemological framework that responds to digital data’s hybrid and processual nature, emphasizing the need for flexible, plural, and reflexive research designs. The work conceptualizes data hybridization as a methodological paradigm capable of capturing active engagement and passive traces through a comparative analysis of digital and computational ethnography, web scraping, APIs, and data donation. The discussion culminates in a typological framework that systematizes data-gathering techniques according to user awareness and researcher intervention, offering practical and theoretical guidance for navigating an increasingly algorithmic and datafied research landscape.