Crossfade: Observing Transitions from Broadcasting to an Algorithmic “Hot Clock” Analysis of Spotify’s Content Curation Algorithms Master Thesis University of Amsterdam Graduate School of Humanities Media Studies: New Media and Digital Culture Author: Oskar Štrajn Student Number: 10849912 E-mail: [email protected] Supervisor: Anne Helmond, MA Second Reader: David Nieborg, PhD Amsterdam, 26 June 2015 ABSTRACT In a society in which digital technology is highly embedded in the everyday life, the processes of automated information forwarding represent a significant form of authority. By observing the current models of content forwarding that employ algorithms, we can identify that these systems are imitating structures already used by the traditional broadcast media. In this thesis, with the help of Spotify’s algorithms as a case study, I present a comparison of algorithms with the traditional FM radio “hot clock”; a schema indicating when particular music selections are to be aired. Furthermore, similar to when broadcast media profoundly influenced popular culture and everyday habits in the pre-internet, software on influences contemporary culture; the latter phenomenon shall be observed in this thesis. KEYWORDS: algorithms, context curation, algorithmic culture, Spotify, music ACKNOWLEDGEMENTS: I would like express my deepest gratitude to everyone who encouraged, supported and assisted me to writing this thesis. I would also like to extend special thanks to my supervisor Anne Helmond for guiding and advising me on how to express my thoughts. Mojca and Zmago Štrajn Blaž Blokar Gabi Rolih Klemen Šali Aleks Jakulin, PhD Melita Zajc, PhD TABLE OF CONTENTS 1. INTRODUCTION 6 1.1. CLOUD MUSIC SERVICES 7 1.2. BRIEF HISTORY 10 1.3. DIGITAL MUSIC UBIQUITY 11 1.4. MUSIC STREAMING PLATFORMS ARE SOFTWARE 12 1.5. SOFTWARE AND ALGORITHMS 13 1.6. RESEARCH QUESTION 16 1.7. OVERVIEW 16 2. LOOKING INTO THE SOFTWARE 18 2.1. THE ERA OF ALGORITHMS 18 2.2. RECOMMENDATION ALGORITHMS OF STREAMING MUSIC SERVICES 21 2.3. SPOTIFY’S RECOMMENDATION ALGORITHMS 22 2.3.1. PERSONALIZATION ALGORITHM 23 2.3.2. CONTEXT ALGORITHM 27 3. METHODOLOGY 31 3.1. INTERFACE ANALYSIS 31 3.2. ALGORITHM ANALYSIS 33 3.3. LIMITATIONS 38 4. CONTEXT AND CURATION 39 4.1. FRONT-END INTERFACE ANALYSIS 39 4.1.1. FEATURED PLAYLISTS ANALYSIS 42 4.1.2. ANALYSIS SPONSORED CONTENT ALGORITHMS 45 4.1.3. INTRODUCING SPOTIFY RUNNING 47 4.2. BACK-END API DATA ANALYSIS AND CONTEXT-CURATED RECOMMENDER 48 4.2.1. ADVANCED CONTEXT-CURATED RECOMMENDER 53 5. THE PAST IS THE FUTURE 55 5.1. THE CURATORS 55 5.2. MEDIA SOFTWARE AND TRADITIONAL BROADCAST STREAM 57 5.3. MOVING BEYOND BROADCAST MEDIA 59 6. CONCLUSION 61 7. BIBLIOGRAPHY 64 8. APPENDIXES 69 8.1. APPENDIX 1 - RESEARCH SOFTWARE CODE 69 8.2. APPENDIX 2 - RESEARCH SOFTWARE FULL DATASET 69 8.3. APPENDIX 3 - EXCEL COLOR CODED DATA 69 TABLE OF FIGURES FIGURE 1 – ALGORITHMS DIAGRAM 14 FIGURE 2 - SPOTIFY DISCOVER FUNCTIONALITY 24 FIGURE 3 – FEATURED PLAYLISTS SECTION 28 FIGURE 4 –PLAYLIST CREATORS 29 FIGURE 5 - CREATION OF RESEARCH ACCOUNT 32 FIGURE 6 – SCRAPING ALGORITHM SCHEME BY SANDVIG AT. AL. 33 FIGURE 7 - SPOTIFY CONTEXT TARGETING FOR BRANDS 34 FIGURE 8 - SPOTIFY DEVELOPER APPLICATION 35 FIGURE 9 - RESEARCH SERVER ROOT 35 FIGURE 10 - VIRTUAL PRIVATE SERVER (VPS) 36 FIGURE 11 - COLOR-CODED DATA IN EXCEL 37 FIGURE 12 - MAINTENANCE ANNOUNCEMENT 38 FIGURE 13 - SPOTIFY OPENING SCREEN 40 FIGURE 14 - DISCOVER WITHOUT USER’S LISTENING HISTORY 41 FIGURE 15 - BROWSING THROUGH SIMILAR ARTISTS 42 FIGURE 16 - SECTIONS OF FEATURED PLAYLISTS 43 FIGURE 17 - FEATURED PLAYLISTS EDITORS 44 FIGURE 18 - SPONSORED PLAYLISTS SUBJECTED TO CONTEXT ALGORITHM 45 FIGURE 19 - USERNAME CHANGE WHILE LOADING PLAYLIST 46 FIGURE 20 - CHART GRAPH OF PLAYLIST GROUPS WITHIN THE RESEARCH WEEK 48 FIGURE 21 - EXCEL COLOR FILTER 49 FIGURE 22 - PARTY PLAYLISTS 50 FIGURE 23 - GETTING READY AND COMMUTE PLAYLISTS 51 FIGURE 24 - OVERVIEW OF THE DATA 52 FIGURE 25 - CONTEXT DATA FROM 27TH OF APRIL 54 FIGURE 26 - ROAD TRIP PLAYLISTS 58 5 1. INTRODUCTION In a society in which digital technology is highly embedded in everyday life, the processes of automated information forwarding representing a significant form of authority. By observing the current models of content forwarding that employ algorithms, we can identify that these systems are imitating structures already used by the traditional broadcast media. In this thesis, with the help of Spotify’s algorithms as a case study, I present a comparison of algorithms with the traditional FM radio “hot clock”; a schema indicating when particular music selections are to be aired. Furthermore, similar to when broadcast media profoundly influenced popular culture and everyday habits in the pre-internet, software on influences contemporary culture; the latter phenomenon shall be observed in this thesis. In recent years, the usage of cloud music services has rapidly expanded, promising a new era of listening. Like physical music media, digital ones have also experienced an evolution. Here the discussion is not only about the development of new file types (.mp1, .mp2, .mp3, .flac, etc.), but about vast music databases managed by a music streaming platform. When I am referring to online streaming media systems, I mean those that offer music as their service. In particular, I am looking into already established platforms, the aim of which is to replace direct online file exchanges. The databases that offer the direct access to content are called “cloud databases”. As all digital technology is moving towards cloud computing, music listening is also following this trend (“European Cloud Computing Strategy”). The first music streaming platforms that started to offer online streaming were introduced in the early 2000s, although they only recently have become widely used, as explained in his reviews of a music cloud service (Haupt 132). In this thesis, I also refer to the music cloud services with the terms “social music platforms” or “music-streaming platforms or services”. Cloud music services, such as Pandora, founded in 2000, offering the first personalized online radio, and the music social network Last.fm, founded in 2001, introduced themselves to the public as a modern supplement to radio stations, but without the possibility of being ubiquitous like the traditional radio. Nevertheless, they offered something completely new: a personalized experience of radio streaming (Haupt 132). Last.fm did not attain great commercial success, although it was very popular from 2006 and 2008. By the end of the latter year, it had made a loss of two million pounds (UK) loss, so they chose to close its radio service and focus only on music recommendations (Sweney). Despite Last.fm no being able to create a digital environment that would attract a great 6 number of users, a new cloud music service, Spotify, founded in 2006, has experienced significant success (Haupt 138). Once that the first streaming music platform experienced success, other services also became interesting for users. As introduced in the IFPI Digital Music Report 2014, streaming and subscription platforms are currently overtaking the music market, mainly by global brands such as Deezer and Spotify (“Digital Music Report — IFPI — Representing the Recording Industry Worldwide”). There are a number of reasons streaming services have become a matter of interest for users. Streaming music platforms offer legal on-demand music, which only became possible after the Internet. Before, one would need to own all music releases on physical media to have the same experience. However, this was already possible in pre-streaming times, where music was available through illegal channels. Music streaming platforms here helped with legalization by paying the artists royalties and aiding in the organization of an extensive content database. This thesis is concerned with the concepts of content “curation” being made by the platforms. Curation is a process of organizing and displaying information relevant to the user. Specifically, within the music streaming platforms, I address the curation of content that is displayed by software. In order for them to function, the music streaming services are using algorithms to present the information; due to the popularity of these services, algorithms are becoming influential in the creation of popular culture. 1.1. CLOUD MUSIC SERVICES Cloud music services appeared as a result of the evolution of digital technology and have resulted in the creation of new ways of music consumption. The first services appeared in 2002 and introduced a new digital format that allowed listening music without transferring the files to a personal device. This can be seen as a next step in the development of music formats, from vinyl to track recordings, from cassettes to CDs and minidiscs, mp3 files, to online streaming. Streaming is still based on digital music formats, as the music streamed is saved in mp3, wav, or similar files. The main difference here is that the principle of music ownership has changed, as the files are not located on the user’s computer, but inside a digital cloud. The interest in the creation of music streaming services appeared due to the disruptions in the music industry created by online piracy. Streaming music platforms managed to create an environment that was capable of managing artists’ 7 rights while simultaneously satisfying users’ expectations of free music on-demand. By free here I have in mind, add-supported music streams. This means that any user is able to play any song at any time or place, as if the user would own the entire database. Another advantage that cloud services provided was the possibility of accessing the cloud database whenever an Internet connection was available. This advantage that was offered by music streaming platforms (the possibility of streaming music at any time or place) made this software as ubiquitous as traditional radio. Cloud music services or streaming music platforms are a combination of two different fields within the creative industries (Ahvenniemi et al.