To identify participants to play online games; to identify relevant connections to the user for inclusion in her social network; to display a listing of photos of persons having highest relevance to the user; to otherwise display or list an identification of persons having highest relevance to the user; to identify persons with whom the user can engage in an instant message or chat session; etc. (Chen et al. 2014)
Above all, top friends are used to prioritize information associated with them above others. Top friends are made-up people insofar as “those kinds of people would not have existed, as a kind of people, until they had been so classified, organized and taxed” (Hacking, 2007: 288). The subset of algorithmic top friends can be seen as a new category of people, emerging in the age of programmed sociality and algorithmic life. There are many more. As the notion of top friends shows, computable friendships hinge on measuring and evaluating users in order to be able to determine their friendship status. While friendships have always been qualitatively determined, as the notion of “best friend” suggests, the extent to which Facebook now quantifiably produces and classifies friendships works to dehumanize sociality itself by encouraging an empty form of competitiveness. Like most social media platforms, Facebook measures social impact, reputation, and influence through the creation of composite numbers that function as a score (Gerlitz & Lury, 2014: 175). The score is typically used to feed rankings or enhance predictions. The computing of friendships is no diferent. In another patent application Facebook engineers suggest that the value of friendship is not confined to users but also serves an essential role in sustaining the social networking system itself. As Schultz et al. (2014) suggest, beter-connected users tend to increase their use, thereby increasing advertising opportunities. A socalled friendship value is not only computed to determine the probability of two users “friending” but also to make decisions as to whether to show a specific advertising unit to a user. The higher the score, the “beter” Facebook deems the friendship to be, increasing the likelihood of using the connection to help promote products. The value of a friendship is produced as a composite number based on a “friendship score, sending score, receiving score or some combination of the scores as determined by value computation engine” (Schultz et al., 2014). According to Schultz et al., “the sending and receiving scores reflect the potential increase in the user’s continued active utilization of the social networking system due to a given connection” (2014: 2). From a computational perspective, friendships are nothing more than an equation geared toward maximizing engagement with the platform.
Far from loving the friend for the friend’s own sake, which would be exemplary of the Aristotelian notion of virtue ethics and friendship, Facebook “wants” friendships to happen in order to increase engagement with the social network, ultimately serving revenue purposes. The quantification and metrification of friendship are not merely part of how connections are computed by Facebook’s algorithmic infrastructure but increasingly make up the visuals of social networking systems through the pervasive display of numbers on the graphical user interface. With more than 3 billion likes and comments posted on Facebook every day, users are both expressing their sentiments and engagement and reminded and made aware of these actions and afects through the visual traces thereof. As the software artist Ben Grosser notes, “A significant component of Facebook’s interface is its revealed enumerations of these ‘likes,’ comments and more” (Grosser 2014: 1). Grosser questions whether people would add as many friends if they were not constantly confronted with how many they have or whether people would “like” as many ads if they were not always told how many others have liked them before them. Grosser’s artwork The Demetricator is a software plugin that removes all metrics from the Facebook interface and critically examines these questions. According to Grosser, Facebook draws on people’s “deeply ingrained ‘desire for more’ compelling people to reimagine friendship as a quantitative space, and pushing us to watch the metric as our guide” (Grosser, 2014). The pervasive enumeration of everything on the user interface function as a rhetorical device, teaching users that more is beter. More is also necessary if we consider the operational logics of the platform. The drive toward more is evident when considering the value of friendship, given that more friends increases the likelihood of engaging with the site. Friends are suggested based on mutual friends, but also on factors such as low activity or few friends. The idea is that, by suggesting friends with low activity level, Facebook “can enable those users to likely have more friends as a result of being suggested [. . .] and thereby likely increasing the candidate user’s engagement level with the social networking system” (Wang et al., 2012: 6). Friendships, then, are variously made and maintained by humans and non-humans alike. The specifics of how friendships are susceptible to computation is immeasurable in and of itself. The purpose, however, of explicating the term “programmed sociality” as core to understanding algorithmic life is to draw atention to software and computational infrastructure as conditions of possibility for sociality in digital media.
Programmed Sociality
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