Although we’ve spent a lot of time writing articles and presenting our work at conferences, the Valuing Electronic Music project was never just about producing a piece of research for academic consumption. Our aim has always been to learn about how music is valued in the age of the internet, and to communicate what we learnt in a form that would be useful to the people most directly affected. The result is our public report. This is a short booklet with everything a musician needs to know about our research. It focuses on our main finding, which is that – even with free digital distribution via websites such as SoundCloud – real-world location remains incredibly important.
Our first peer reviewed article is also available for free download, along with other documents, via the reports page of this website.
The first peer-reviewed journal article arising from the Valuing Electronic Music project has now been published in Cultural Trends as part of a special issue on empirical research into cultural value guest-edited by Dave O’Brien. It focuses on a key finding of the project: even though musicians can now distribute their music for free via the internet, their real-world location remains hugely important. Through qualitative research, we found that electronic musicians in London (a) considered themselves to benefit from being based in that city, and (b) considered a particular part of that city (the highly gentrified, ‘hipsterish’ district of Shoreditch and its immediate surroundings) to be particularly advantageous for less commercial kinds of music. Through quantitative research, we found SoundCloud users based in London to occupy a position at the centre of a network of ‘following’ relationships in which the next best locations appeared to be New York and Los Angeles. Our findings are consistent with the view that the 21st century ‘new media’ produce similar exclusions to the ‘big media’ of the 20th century and do not create anything resembling a level playing field between signed and unsigned artists, provincial and metropolitan scenes, or the developed and the developing world.
The article is open access so please download the full text to read for yourself.
Allington, D., Dueck, B., and Jordanous, A. (2015). ‘Networks of value in electronic music: SoundCloud, London, and the importance of place’. Cultural Trends 24 (3): pp. 211-222.
Small version of the poster I put together for today’s pop-up research event at the Open University. Node size indicates total number of followers for SoundCloud users based in each city; arrows indicate where those followers come from (so far as we can tell); node colour indicates centrality to the network of these relationships (by eigenvector centrality). If you want more technical details, read last September’s post.
Date: 26 November 2014
Venue: Arts Music Studio, The Open University Walton Hall, Milton Keynes
Daniel Allington (Department of Applied Linguistics and English Language, Open University)
Byron Dueck (Department of Music, Open University);
Anna Jordanous (School of Computing, University of Kent)
Back in June, we held our first public event, with live music performances, talks, and free food. The talks and performances were recorded and soon they will all be available online. We’re starting with Anna’s, Byron’s, and my introduction to the Valuing Electronic Music project as a whole. In this talk, we explain how and why we have been studying the value of electronic music, and reveal a little of what we’ve found out so far.
Also available from the Open University podcast site.
On 23 September 2014, I gave an invited presentation to a meeting of the Creative Data Club, organised by Sound and Music, the national agency for new music. Also speaking were Chris Unitt from One Further, presenting a study on how arts organisations are using Facebook, Jay Short from inition, showcasing 3D-printed visualisations of social media data, Dan Simpson, talking about his crowdsourcing of poetic composition, and shardcore, telling the hilarious and poignant tale of Alex the twitterbot. Here are the slides, plus a few audience responses and livetweets at the end. The text is based on the same handwritten notes that I extemporised from on the day.
(Photograph by Sound and Music)
Sometimes we just want to get a simple overview of the types of things people are saying. In the case of our SoundCloud analysis, we want to know what people are saying about each other’s tracks.
We’ve made use of http://www.wordle.net/ word clouds to get an overview: what words are people typically using in comments on SoundCloud? Are they positive? descriptive? critical? irrelevant?
Wordcloud of comments taken from a random sample of 150000 SoundCloud users’ comments. Generated using http://www.wordle.net
Wanting to find out what was typical SoundCloud behaviour – as opposed to what our case study users were doing – we took a random sample of 150000 SoundCloud accounts earlier this year and downloaded their profile data, plus the profile data of everyone they were following (plus some other stuff, but that’s for another time). One of the things we did with this data was to construct a social network graph showing ‘follow’ relationships at city level: every time our computer program found that a sampled user self-identified with city A followed a user self-identified with city B, it created an ‘arc’ (represented with an arrow) from city A to city B. We then combined all the arcs so that instead of, say, 2000 arcs from city A to city B, there would now be a single arc with a ‘weight’ of 2000. We then imported this data into Gephi, sized the nodes representing cities to reflect the total weight of all the incoming arcs, positioned them with the Force Atlas algorithm, and used the Louvain community detection method to identify ‘clusters’, where a cluster is a group of nodes that are better connected to each other than they are to nodes from outside the group. And here’s the result, with five colours to represent the five clusters.
In my previous post on this topic, I introduced a problem – how to understand the work that explicit genre categorisations are made to do by people uploading tracks to the SoundCloud audio-sharing website – and a potential solution – identifying the three categories most frequently used by each individual in a sample and studying regularities in the ways in which pairs of categories tend to pop up within the same group of three. I also presented some partial and preliminary findings in the form of a matrix comparing co-occurrences of the five genre categories most frequently used by people within an initial sample. And I either glossed over or left unmentioned a slew of problems, some of which we’ve been more successful in addressing than others at present (because these are only blog posts, and we haven’t finished the research yet). The biggest problem is the sample itself: the analysis was done on the basis of a snowball sample, when a random sample would be more appropriate. Hence the provisionality of all this. The analysis will be redone soon on the basis of a sample that will enable us to make more robust claims, but in the meantime I wanted to share our thought processes and working methods with the world because – quite apart from anything else – I’m excited about the patterns that are emerging.
One of the problems you’re always going to face when studying electronic music is the need to decide what you think ‘electronic music’ means. It’s a question of genre, and as Paul DiMaggio acknowledged in one of his most influential papers, genre is at once a formal and a social concept: