Introduction
Programmed Sociality
Let us for a moment consider the following scenario: Copenhagen on a rainy November day. The semester is coming to a close. Students and professors alike are stressed, but it is nonetheless a sociable time of the year. Conference trips, essays to be graded, dinner plans, and Christmas presents in the pipeline. In other words: buying plane tickets, surfing the Web for restaurant tips, streaming music, coordinating dinner plans with friends, shopping for Christmas gifts, preparing lectures, watching a movie after work. Besides describing a random day in an academic life, all these activities imply using the Internet. I found some cheap tickets using a metasearch engine, discovered a nice restaurant through a collaborative filtering system, listened to a music playlist suggested to me by a music-streaming site, bought a gift for my mother at an online designer store, chated with friends on a social-networking site to find a suitable time for a pre-Christmas dinner, and, finally, watched an episode of my favorite TV series on a movie-streaming site.
It is not just a story about the good life of a Scandinavian academic. It suggests a life deeply entwined with media. Following media theorist Mark Deuze (2012), we might say that life is not only lived in and through media but in and through specific types of media. What these activities have in common is a high degree of interaction with algorithmic media, media whose core function depends on algorithmic operations. This book starts from the premise that life is not merely infused with media but increasingly takes place in and through an algorithmic media landscape. Key to this premise is the notion of the co-production of social life, practices, and technology. While people interact with specific media companies and platforms, these platforms interact with people as well. Users do not simply consult websites or talk to their friends online. Social media and other commercial Web companies recommend, suggest, and provide users with what their algorithms have predicted to be the most relevant, hot, or interesting news, books, or movies to watch, buy, and consume. Platforms act as performative intermediaries that participate in shaping the worlds they only purport to represent. Facebook is not simply a social networking site that lets users “connect with friends and the world around you.”1 As media scholar José van Dijck has argued, “Social media are inevitably automated systems that engineer and manipulate connections” (2013: 12). By the same token, Netflix is not a website that lets users “see what’s next and watch anytime, cancel anytime.”2 It can’t be seen as a neutral platform that merely queries its vast database about a user’s request to show the movies they explicitly want to watch. Relying on vast amounts of data, Netflix algorithms are used to analyze paterns in people’s taste, to recommend more of the same. Popularity is not only a quantifiable measure that helps companies such as Facebook and Netflix to determine relevant content. User input and the paterns emerging from it are turned into a means of production. What we see is no longer what we get. What we get is what we did and that is what we see. In the case that Netflix suggests we watch House of Cards, it is largely a mater of consumers getting back their own processed data. When the show was released in 2013 it quickly became a cornerstone for data-driven programming, the idea that successful business decisions are driven by big data analytics.
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