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Jordanous, Anna; Allington, Daniel; Dueck, Byron (2014)
Languages: English
Types: Unknown
Subjects: BH, H1, M1, QA75, ZA4050
To what extent can we use social network activity to examine the value that electronic musicians place in each other’s work? This talk reports on the work of the Valuing Electronic Music (VEM) project, documented online at http://valuingelectronicmusic.org. During the project we combined ethnographic observation and interviewing in London’s electronic music scene with automated collection of quantitative data from the popular SoundCloud website. The talk at DMRN will reflect upon the findings of the research by the valuingelectronicmusic.org team, which have implications for our understanding and approximate measurement of the cultural value of electronic music. We shall also reflect on the challenges and rewards of combining situated qualitative research with quantitative analysis of large datasets gathered online, as well as our ongoing research and future plans.\ud \ud Social networking and new media websites have provided music makers with new spaces in which to negotiate and produce cultural value for their work, taking on tasks that would once have been the sphere of specialists in marketing, publicity and criticism. These phenomena appear to have had a particular impact on electronic music, which is typically made by lone, but highly networked, individuals and is often circulated non-commercially online, for example on http://www.soundcloud.com. \ud \ud We found that while certain kinds of activity on SoundCloud have little apparent economic value (e.g. commenting on each others’ tracks, publishing free downloads) these activities seem to generate cultural value that facilitates more economically valuable work. For the most part, music-makers assert their concern for all listeners, but close attention to their activity (and how they describe it) suggests that interactions with peers (i.e. fellow music makers) are especially important for the production of value for their work. \ud \ud In conjunction with the findings from interviews with electronic musicians, we used computational analysis to study interactions in social networks. Through such analysis we extrapolated information about how musicians interact with each other on SoundCloud, and how they express appreciation of each other’s work. Typically, it was more productive to study clusters of strongly connected cliques within the SoundCloud network, rather than a sample of the entire network. Using centrality measures such as PageRank or indegree analysis revealed little novel information beyond highlighting accounts representing artists that were very successful commercially; but the value of such artists’ work was not prioritised by participants in our qualitative research. The SoundCloud user community tends to cluster according to several factors: for example we found empirical evidence of clusters forming around common musical genres, and also of clusters around certain geographical locations such as London which were privileged above others. \ud \ud Drawing from our existing findings, centred around the London music scene, we are currently applying similar quantitative analysis approaches to the Scottish electronic music scene. We hypothesise that such computational analysis of SoundCloud data will allow us to approximate the value placed on different electronic musicians’ work, leading to a methodology to use social network analysis as a proxy for measuring certain types of musical value. Results of this ongoing work will be reported at DMRN.\ud \ud Our findings indicated a complex relationship between the two areas of work referred to as production (a blanket term covering all activities involved in the creation of an audio track) and DJing (combination of audio tracks into a continuous mix). Based on our existing work, we are planning future work to examine how DJs express value of producers’ work through playing their tracks. The quantitative research centres around analysing how DJs play different tracks in their sets, including analysis of the contouring and dynamic shaping of a DJ set, detection of which tracks are played and what changes are made to the tracks. This will be supported by, and will in turn inform, qualitative research undertaken with DJs on how they choose music for their sets. Feedback on both the existing research findings and our future plans from DMRN will be welcome.

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