The Algorithmic Mirror: Knowledge Creation and Self-Perception in Dating Applications

Algorithmic dating applications mediate romance through an "algorithmic mirror," subjecting users to data-driven classifications that shape their self-perception. However, the specific strategies users employ to interpret and strategically manage this reflection remain underexplored. Understanding this dynamic is critical, as navigating the algorithmic gaze demands significant emotional labor and has profound implications for user agency and well-being. Through semi-structured interviews with 15 OkCupid users, I investigated this process of sense-making. I contribute a novel typology of three knowledge forms, Folk, Personal, and Academic, that users construct to redefine themselves against the algorithm. Theoretically, this paper frames the "algorithmic other" as a statistical counterpart to Mead’s "generalized other," revealing a core "dual-audience dilemma" where users perform for both humans and machines. These findings inform the design of more transparent and contestable systems that better support user agency.