On Monday, we submitted a report of our preliminary findings to the AHRC’s Cultural Value Project. Research is still ongoing, and we’re planning an ambitious follow-up study. The report is not available to the public yet – and in any case, the whole thing ran to 69 pages, plus covers etc – but here’s a taster.
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.
Valuing Electronic Music 