Taking Blued browsing and live streaming as two data-structured and algorithm-guided dating sites, this article explores the ways through which dating goals are algorithmically shaped through data gaming. It also documents how Blued users attribute values and meanings to the dating metrics to the extent that they become cultural objects themselves in the algorithmic sociality (Gillespie, 2016b ; Roberge Seyfert, 2016 ).
Methods
On Blued, five buttons, ‘Browse’, ‘Chat’, ‘Buzz’, ‘Live’, and ‘More’, line up at the bottom of the user interface (observed on ). The author has been a Blued user since . During the last three years, I have habitually opened Blued on an almost daily basis to observe its updates of features and interfaces as well as to engage in casual chat with nearby users. For this reason, this article employs the walkthrough method of Light, Burgess, and Duguay ( 2016 ), which has been specially tailored for studying apps. According to Light, Burgess and Duguay, the walkthrough method asks researchers to engage directly with an app’s interface, examining its technological mechanisms and embedded cultural references to understand how it guides users and shapes experiences. They further explicate that the key to this method is to observe step by step and to document an app’s screens, features, and flows of activity. In particular, the walkthrough method consists of three stages: ‘registration and entry’, ‘everyday use’, and ‘app suspension, closure and leaving’ ( 2016 , p.12).
In everyday use, I consciously applied ‘an analytical eye’ (Light et al., 2016 , p.11) as a researcher from to distinguish this use of the app from my previous personal use. During this time period, I browsed user profiles randomly and watched live streams on the first page of the Blued live channel. I collected data by making screenshots of profiles and live streams where users’ data gaming came up. The collected data contain statements on numbers and tags that users make to include in and exclude from their dating preferences as well as viewers’ comments that sexualize the specific bodily parts of gay broadcasters during live streaming.
Despite the handy merits of the walkthrough method of studying apps, there exist potential ethical concerns. For example, it is unlikely that users’ informed consent can be obtained since the walkthrough ‘avoids interaction with users’ (Light et al., 2016 , p. 15). To remedy this defect, Light et al. ( 2016 ) suggest the anonymization of user data on the one hand and conducting user interviews on the other. I took both pieces of advices into consideration because the concern of this article is not with how app developers design algorithms in the background but how users game and play with data through filtering, sorting, and evaluating to shape the algorithmic dating results.
Following this method, the author reinstated the registration process by opening a new account on Blued in e-sex dating preferences are datafied
Blued has a function allowing users to reposition themselves through a built-in map to interact with others over long distances. In , to choose potential interviewees, I pinned my Blued location to Beijing Sanlitun and Worker’s Stadium, which are considered to be gay hubs of the city. In these two areas, I randomly collected and screenshot 108 user profiles whose textual self-introductions contain numbers, tags, and descriptions of physical features. I sent these 108 users a message individually to ask https://datingranking.net/mindful-dating/ if they were interested in participating in this study. Seventeen people (aged 20–36) got back to me with a positive response. After acquiring their consent to use the information they displayed on Blued under pseudonyms, I sent them a web link to a page where they could answer questions on how they filter and sort data to socialize on Blued.
