The evidence that ideas and fashions spread through society like viruses or like wildfire is compelling. Numerous studies have examined the networks in which this spread takes place and with increasingly large data sets to work with, researchers have become increasingly confident in their network-centric view of the world. These tools are teasing apart the large scale behaviour of humanity in ever increasing resolution.
In the fashion world, London, New York and Paris are generally considered the leaders that everyone else follows. So an interesting question is whether network science can tell us which cities play a similar role for music.
That’s exactly the question that Conrad Lee and Pádraig Cunningham at the Clique Research Cluster in Ireland set out to answer by analysing data from Last.fm, an social website for music.
Last.fm is interesting because it publishes lists of the most listened to artists divided geographically. So if you live in Seattle, for example, you can see what people in your area are listing to.
So Lee and Cunningham have studied the way these charts vary in time and looked to see whether some cities consistently lead others in terms of listening habits.
These guys studied the Last.fm data for 200 cities around the world dating back to 2003. This is compiled from some 60 billion pieces of data that Last.fm collects from its users.
This is a noisy data set. Some cities have so few listeners that their data is hard to distinguish from noise. So Lee and Cunningham have to apply some fairly robust cleaning techniques to remove this noise.
They then use recently developed statistical techniques to decide which cities lead others. They then construct a network in which a link pointing from one city to another indicates that one follows the other.
The results are interesting. They show that certain cities appear to lead others for various genres of music. For example, Montreal seems to lead North American in indie music listening habits and the leader for hip hop is Atlanta. In Europe, Paris leads for indie music whereas Oslo leads for music as a whole.
There are other interesting patterns too. For example, cities that have similar listening habits are not linked in this network. For example, Portland and San Francisco; Cracow and Warsaw; and Birmingham and Manchester.
Lee and Cunningham suggest that when two cities’ listening habits are synchronised there is little to be gained from following the listening habits in the other city so residents look elsewhere.
There’s another interesting pattern. It’s easy to imagine that the biggest cities ought to be those furthest ahead of the curve because they have biggest populations from which new and interesting bands can emerge. That doesn’t seem to be the case in this data–big cities such as New York, LA and London do not lead. “We find only weak support for this hypothesis,” say Lee and Cunningham.
That may cause some alarm bells to ring. An interesting body of work has recently suggested that big cities benefit disproportionally for their size since qualities such as efficiency, productivity and innovation all scale super linearly with population.
An important question for Lee and Cunningham is why that doesn’t hold for music too.
There is also a question over whether the trends that Cunningham and Lee have found really reflect their hypothesis that some cities’ listening habits lead others. Humans are notoriously good at finding patterns in random data.
The ultimate test, of course, is whether their discovery has any predictive value. For example, could they predict how listening habits will change in the near future? “We have not yet demonstrated that our models have this predictive power, although we plan to attempt this validation in future work,” they say.
So we must wait and see. If they manage any kind prediction based on this work, it’ll be an impressive feat.
In the meantime, if you want to know what you’re likely to be listening to in the near future, cough, tune in to the music now playing in Atlanta and Oslo.
Ref: arxiv.org/abs/1204.2677: The Geographic Flow of Music
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