“Big data see through you”: Sexual identifications in an age of algorithmic recommendation

Abstract

On Douyin, China's domestic version of TikTok, straight users are being recommended queer content, and videos created by straight men are being recommended to queer viewers. As Douyin recommends videos based on users’ online activities (e.g., liking, sharing, or spending time watching a video) and networks (e.g., connections made on the platform), sexuality comes to be algorithmically interpreted and defined. This process differs from an understanding of gender and sexuality as more or less fixed classifications that are the result of what people register when entering a platform concerning their gender and sexual identifications. This article analyzes viewers’ and creators’ experiences and reflections on the algorithmic grouping of sexual orientation and erotic curiosity through the relational lens of configurations. Using two years of online observational data and in-depth interviews with 18 Douyin users, both straight and same-sex orientated, we found that a language-centered, semiotic approach alone cannot assist in capturing the reconfiguration of sexual identifications presently occurring in China, and most likely elsewhere. Sexual identifications are the result of a relational process in which desires (regardless of sexual orientation) and intimacy, content creation and consumption, platform vernaculars and affordances, and data and algorithms converge and clash. This process allows for an erotic curiosity that has not yet been named or normalized in language systems and therefore reconfigures how sexual identity or orientation come to be understood in relation to the ever-increasing presence of computational power.

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