Algorithmic Individuation on Spotify
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MA Thesis New Media and Digital Cultures University of Amsterdam Student: Jan Zuilhof Student nr: 6152139 Date of completion: 14-11-2014 Supervisor: Michael Dieter Second Marker: Marc Tuters The Soundtracked Self: Algorithmic Individuation on Spotify 1 I would like to thank Michael Dieter for supervising this thesis. The conversions that we have had about the topic and relevant literature were truly insightful and have helped me a lot. His patience is also very much appreciated. Although he left the University of Amsterdam before I did, he continued the supervision process, for which I am very grateful. 2 INTRODUCTION 4 1. CAPTURING ATTENTION: LEISURE OR LABOUR? 6 1.1 INTRODUCTION 6 1.2 DEMOCRACY ON THE WEB 6 1.3 EXPLOITED PROSUMERS 8 1.4 ALIENATION: THE DOUBLE LOGIC OF PROSUMPTION 10 1.5 CONCLUSION 13 2. INDIVIDUATION IN THE COMPUTATIONAL INDUSTRIES 15 2.1 INTRODUCTION 15 2.2 PSEUDO-INDIVIDUATION IN THE CULTURE INDUSTRY 15 2.3 RECORDING OF TRACES: ASSOCIATION 20 2.4 CONCLUSION 24 3. ALGORITHMIC RECOMMENDATION: INDIVIDUATION THROUGH SPOTIFY 25 3.1 INTRODUCTION 25 3.2 BEATS IN BITS 27 3.3 SOUNDTRACKING YOUR LIFE 31 3.4 DOWN TO BUSINESS 40 CONCLUSION 46 LITERATURE 49 3 INTRODUCTION "Maybe with motion sensors in phones, we can start guessing things like 'are you running, biking or driving?' Maybe it has a temperature sensor, or a heart rate sensor so we can get a sense of whether you're tense. Maybe it connects to some other services, for example if we know more about your sleeping habits we know what time you're likely to go to sleep or what time you wake up it can be personalised." -Donovan Sung- Product manager at Spotify (Smith) When my girlfriend and I wake up in the morning, we will sometimes listen to the radio. Whether it plays mostly depends on how we communicate: “shall I turn it on?... shall I turn it off?... should it be louder? The decisions that we make are based on the opinions of two humans that either have to debate, or agreed upon it in the first place. Whichever one of those it is, we have explicitly expressed how we feel about it, and by this have made it into a collaborative practice. During the day, many more of these types of situations happen. When friends come over for diner, should there be music playing in the background? And if yes, which music will every one like at least to some extent? What radio station do the most people like to listen to at work? All of these things can be discussed, and people can communicate their preferences in a way that everybody understands. In the past years, however, another approach for selecting music has also gained popularity. Whereas humans have been curators during the first century of recorded music, this can now also be delegated to algorithms. Although both human-to- human and algorithmic recommendations rely on the preferences of people, they also differ from each other; algorithms can process information in ways that go beyond human comprehension. This thesis is about how such ways influence individuation. To look for those implications, Spotify is used a case study because the service has caught much attention in the past years and is explicit about its motivation, which is revitalizing the music industry. Why Spotify is also important is its data driven approach. In the quote on the top of this page, Spotify’s Donovan Sung speaks about possible future directions that Spotify might go in. Although this is still speculation, it does signify that the extraction of data might get even more abstract than it sometimes already is now. This abstraction is an important thread for my project here. In a sense, my approach is quite the opposite of 4 Spotify’s, which means that I will look for answers that cannot be found in data alone but should be searched in theorizations. The research question that will guide me through this thesis is: how do the discursive and technological dimensions of Spotify influence individuation? To find an answer to this question, I will treat how relations between humans and web platforms can be theorized terms of labour in the first chapter. Concluding this chapter, I will offer a definition of the widely used concept of prosumption. In the second chapter, I will focus on theories about how the distribution of media influences individuation. This chapter also functions to introduce the vocabulary of associated and dissociated milieus, which will be used later. The case study of Spotify will be treated in the third and final chapter. For this analysis, the theories about prosumption, association and dissociation will be used as a framework. 5 1. CAPTURING ATTENTION: LEISURE OR LABOUR? 1.1 Introduction In this chapter, I will discuss the first part of the theoretical background for this thesis. Understanding relevant debates about the economy on the web and its influence on subjectivity is crucial for the analysis of Spotify that will follow in the third chapter. I will begin by briefly sketching out why the democratic and emancipatory character that the web sometimes seems to have should not be taken for granted, but instead examined critically. Next, I will move into a more specific direction, which is a Marxist description of the economy on the web. Doing so, I will show that the traditional Marxist concepts of exploitation and alienation have undergone a transformation, yet are still present in today’s digital structures. This chapter also functions as an introduction to the second chapter, where I will go further into the influence of corporate orchestrated media on subjectivity. 1.2 Democracy on the Web The web offers many opportunities for people to communicate and express themselves. However, these opportunities are restricted by technological rule sets. User generated content is not only information produced by an individual, but also a composition of the rule set of a specific platform and an act or acts performed by people. There are numerous examples of websites and –applications that allow users to perform an act that influences what happens in that particular space. Social media like Facebook enable people to interact with each other, and potentially anything that can be expressed in written language can be discussed there. Instagram lets its users take any picture of the real world and upload it to their database. The rhetoric of these types of platforms often suggests that anything can be done. Facebook states that its mission is to “give the people power and make the world more open and connected” (“About Facebook”). Instagram lets people share their lives in an easy way for free (“Instagram”). But the way in which these platforms are set up contributes to what can and cannot be done. While a person can decide what status update he or she will like or comment on, it is Facebook that has decided that you can like- or comment on a status update. On Instagram you can share every moment of your life, but Instagram also arranges the set of filters you can choose from so your friends will see your life in a very ‘Instagram way’. 6 So although people obviously contribute to web platforms in their own way, the spaces and conditions through which they move partly shape their roles. Furthermore, this is complicated by the way various platforms are interconnected. Music platforms such as Spotify and Soundcloud, or video platforms such as YouTube and Vimeo all encourage their users to ‘share’ content on social media like Facebook and Twitter. Facebook and Twitter even encourage their users to link content between the two platforms, so that a message posted on one will also appear on the other. All of these platforms work according to their own logic and follow their own business model, and both them and their users have their own agendas. The result of this is a complicated system of spaces, structures, and acts, in which various actors with different needs move. What this system – the web - is has been theorized in various ways in the past decades. One term that has resonated for years was ‘web 2.0’ (O’Reilly 2007). It was used to describe a new structure of the web in which companies “leverage customer-self service and algorithmic data management to reach out to the entire web” (ibid: 21), and “network effects from user contributions are the key to market dominance” (ibid: 24). While O’Reilly mainly writes about business models, the same shift has also been noticed in cultural studies. In Convergence Culture (2006), Henry Jenkins argues that new media reshape old media. He acknowledges that these changes are corporate driven, but he also writes that it is up to the people whether they are ready for greater participation (ibid: 243). Many more have argued that this phenomenon – often described as ‘prosumption’ – on web 2.0 platforms has changed the role of consumers in the sense that they are more empowered and conscious now, and the web is a largely democratic medium (Andriole 2010: 78, Bruns 2008: np, Deuze 2007: 244, Leadbeater 2008: 6, Lin 2007: 102, Tapscott & Williams 2010: np). There are others, however, that claim that these accounts tend to focus on the aspect of participation without critically examining its implications. Authors should take into account that the web is still ruled by big corporations that turn user generated content into large profits. Van Dijck & Nieborg (2009: 869) argue that Jenkins recognizes the difference between producers and consumers, but his rhetoric is the same as that of business manifestos that do not make this distinction.