Streaming the Romance: Gendered Algorithmic Interpellation on Netflix

Abstract

This paper broadens understanding of the relationship between algorithms and gender by examining how a group of women in Costa Rica relate to algorithmic recommendations on Netflix. Drawing on 25 interviews and an analysis of their Netflix profiles, we examine how this group of women made sense of algorithmic technologies that drew their attention to content associated with ideas of romantic love. We theorize this process as “gendered algorithmic interpellation” or the work embedded in algorithms to “hail” users in particular ways and offer them gendered subject positions. Our analysis centers on four dynamics: personalized interpellation (how users come to believe that they are being addressed in a personalized manner by Netflix); bundled interpellation (how traditional generic cues that guide interpellation have been repackaged in Netflix’s interface); ritual interpellation (the belief that recommendations are a product of past user behaviors); and calculated interpellation (the notion that recommendations are the result of sophisticated algorithmic calculation). We discuss how interviewees both responded to these four dynamics of interpellation and how they resisted them. In this way, we shed light on how algorithmic recommendations can become important means to exploit and worsen gendered structures.