Cracking the tinder code: An experience sampling approach to the dynamics and impact of platform governing algorithms

Abstract

This article conceptualizes algorithmically-governed platforms as the outcomes of a structuration process involving three types of actors: platform owners/developers, platform users, and machine learning algorithms. This threefold conceptualization informs media effects research, which still struggles to incorporate algorithmic influence. It invokes insights into algorithmic governance from platform studies and (critical) studies in the political economy of online platforms. This approach illuminates platforms’ underlying technological and economic logics, which allows to construct hypotheses on how they appropriate algorithmic mechanisms, and how these mechanisms function. The present study tests the feasibility of experience sampling to test such hypotheses. The proposed methodology is applied to the case of mobile dating app Tinder.