Gay Data

Since its launch in 2009, the geosocial networking service Grindr has become an increasingly mainstream and prominent part of gay culture, both in the United States and globally. Mobile applications like Grindr give users the ability to quickly and easily share information about themselves (in the form of text, numbers, and pictures), and connect with each other in real time on the basis of geographic proximity. I argue that these services constitute an important site for examining how bodies, identities, and communities are translated into data, as well as how data becomes a tool for forming, understanding, and managing personal relationships. Throughout this work, I articulate a model of networked interactivity that conceptualizes self-expression as an act determined by three sometimes overlapping, sometimes conflicting sets of affordances and constraints: (1) technocommercial structures of software and business; (2) cultural and subcultural norms, mores, histories, and standards of acceptable and expected conduct; and (3) sociopolitical tendencies that appear to be (but in fact are not) fixed technocommercial structures. In these discussions, Grindr serves both as a model of processes that apply to social networking more generally, as well as a particular study into how networked interactivity is complicated by the histories and particularities of Western gay culture. Over the course of this dissertation, I suggest ways in which users, policymakers, and developers can productively recognize the liveness, vitality, and durability of personal information in the design, implementation, and use of gay-targeted social networking services. Specifically, I argue that through a focus on (1) open-ended structures of interface design, (2) clear and transparent articulations of service policies, and the rationales behind them, and (3) approaches to user information that promote data sovereignty, designers, developers, and advocates can work to make social networking services, including Grindr, safer and more representative of their users throughout their data’s lifecycle.