A comparative study of algorithmic–user classification practices in online dating: a human–machine learning process

Abstract

ABSTRACT Dating-app graphical user interface (GUI) structures for data collection contain categories that enable classifying users algorithmically and classifying users between each other to define their sexuality and find a date. Indeed, app providers define categories that mediate the users’ bodies and interactions to present themselves, and these categories ultimately serve app algorithms for recommending profiles. Categories establish a main reciprocated mediation between algorithms and users that is explored in this article to shed new light on the way Big Data shapes human–algorithmic interactions in online dating. However, online dating research pays little attention to classification processes from the perspective of the user, although classification is key to algorithm function for codifying sexuality. Using a qualitative analysis of 40 participant situated interviews, I examine the way dating-app users make sense of predefined categorical structures and their underlying classification processes, within 26 platforms. The results show that actors learn to integrate algorithmic logic into their common knowledge, as well as to challenge the algorithmic logic, and thus produce new conventions to classify their emotional states, physical attractiveness, and sexual preferences.