Guest blogger Professor Pelle Snickars: “Streaming Heritage: ‘Following Files’ in Digital Music Distribution”

The research project “Streaming Heritage: ‘Following Files’ in Digital Music Distribution”, funded by the Swedish Research Council, is now in its second year. The project team consists of Pelle Snickars (project leader), Rasmus Fleischer, Anna Johansson, Patrick Vonderau and Maria Eriksson. The project is located at HUMlab where developers Roger Mähler and Fredrik Palm do the actual coding.

In short, the project studies emerging streaming media cultures in general, and the music service Spotify in particular (with a bearing on the digital challenges posed by direct access to musical heritage.) Building on the tradition of ‘breaching experiments’ in ethnomethodology, the research group seeks to break into the hidden infrastructures of digital music distribution in order to study its underlying norms and structures. The key idea is to ‘follow files’ (rather than the people making or using them) on their distributive journey through the streaming ecosystem.

Kpc wintergatan222 130507 EBE_MS-Office

Photo: Elin Berge

So far research has focused basically four broader areas: the history and evolvement of streaming music in general and Spotify in particular (Fleischer), streaming aggregation’s politics and effects on value and cultural production (Vonderau), the tracing of historical development of music metadata management and its ties to knowledge production and management that falls under the headline of ‘big data’ (Eriksson), and various forms of bot culture in relation to automated music aggregation (Snickars).

One article has been published, and more preliminary results are to be presented in a number of upcoming articles and conferences during 2016. Eriksson, for example recently submitted an article around digital music distribution increasingly powered by automated mechanisms that capture, sort and analyze large amounts of web-based data. The article traces the historical development of music metadata management and its ties to the field of ‘big data’ knowledge production. In particular, it explores the data catching mechanisms enabled by the Spotify-owned company The Echo Nest, and provides a close reading of parts of the company’s collection and analysis of data regarding musicians. In a similar manner, Johansson and Eriksson are exploring how music recommendations are entangled with fantasies of for example age, gender, and geography. By capturing and analyzing the music recommendations Spotify delivers to a selected number of pre-designed Spotify users, the experiment sets out to explore how the Spotify client, and it’s algorithms, are performative of user identities and taste constellations. Results will be presented at various conferences during next year. In addition, Snickars has continued working with the HUMlab programers on various forms of “bot experiments”. One forthcoming article focuses the streaming notion of “more music”, and an abstract for the upcoming DH-conference in Kraków (during the summer of 2016) is entitled: “SpotiBot—Turing testing Spotify”. It reads as follows, and gives an indication of the ways in which the project is being conducted:

Under the computational hood of streaming services all streams are equal, and every stream thus means (potentially) increased revenue from advertisers. Spotify is hence likely to include—rather than reject—various forms of (semi-)automated music, sounds and (audio) bots. At HUMlab we therefore set up an experiment—SpotiBot—with the purpose to determine if it was possible to provoke, or even to some extent undermine, the Spotify business model (based on the 30 second royalty rule). Royalties from Spotify are only disbursed once a song is registered as a play, which happens after 30 seconds. The SpotiBot engine was be used to play a single track repeatedly (both self-produced music and Abba’s ”Dancing Queen”), during less and more than 30 seconds, and with a fixed repetition scheme running from 10 to n times, simultaneously with different Spotify account. Based on a set of tools provided by Selenium the SpotiBot engine automated the Spotify web client by simulating user interaction within the web interface. From a computational perspective the Spotify web client appeared as black box; the logics that the Spotify application was governed by was, for example, not known in advance, and the web page structure (in HTML) and client side scripting quite complex. It was not doable within the experiment to gain a fuller understanding of the dialogue between the client and the server. As a consequence, the development of the SpotiBot-experiment was (to some extent) based on ‘trial and error’ how the client behaved, and what kind of data was sent from the server for different user actions. Using a single virtual machine—hidden behind only one proxy IP—the results nevertheless indicate that it is possible to automatically play tracks for thousands of repetitions that exceeds the royalty rule. Even if we encountered a number of problems and deviations that interrupted the client execution, the Spotify business model can in short be tampered with. In other words, one might ask what happens when—not if—streaming bots approximate human listener behavior in such a way that it becomes impossible to distinguish between a human and a machine? Streaming fraud, as it has been labeled, then runs the risk of undermining the economic revenue models of streaming services as Spotify.

Finally, during the following weeks the project group will do presentations in the U.S. The first one is called, “Spotify Teardown”, and consists of a project presentation and roundtable at the Center for Information Technology and Society at the University of California, Santa Barbara. On the one hand the presentation will have a focus on methodology, background research and preliminary findings, and on the other hand try to initiate a discussion with three focused areas: (1.) ”Ethical and Legal Limitations”: What are the ethical/legal issues that arise in relation to activist projects, and how to tackle them? (2.) ”Metaphors for Research”: What metaphors are useful, or more useful than conventional metaphors such as “platform” or “platform responsibility”? and (3.) ”New Qualitative Methods and Old Disciplinary Frameworks”: What are the key challenges of working with qualitative, inter- and pelle175transdisciplinary methods in institutional environments? In addition, Pelle Snickars will also do another project presentation in New York at Cuny (The City University of New York) at the conference, ”Digging Deep: Ecosystems, Institutions and Processes for Critical Making”.

Pelle Snickars is Professor of Media and Communication Studies, specialising in digital humanities at Umeå university, with an affiliation to HUMlab.

This entry was posted in Uncategorized. Bookmark the permalink.

Comments are closed.