Abstract
Popular songs are often said to be ‘contagious’, ‘infectious’ or ‘viral’. We find that download count time series for many popular songs resemble infectious disease epidemic curves. This paper suggests infectious disease transmission models could help clarify mechanisms that contribute to the ‘spread’ of song preferences and how these mechanisms underlie song popularity. We analysed data from MixRadio, comprising song downloads through Nokia cell phones in Great Britain from 2007 to 2014. We compared the ability of the standard susceptible–infectious–recovered (SIR) epidemic model and a phenomenological (spline) model to fit download time series of popular songs. We fitted these same models to simulated epidemic time series generated by the SIR model. Song downloads are captured better by the SIR model, to the same extent that actual SIR simulations are fitted better by the SIR model than by splines. This suggests that the social processes underlying song popularity are similar to those that drive infectious disease transmission. We draw conclusions about song popularity within specific genres based on estimated SIR parameters. In particular, we argue that faster spread of preferences for Electronica songs may reflect stronger connectivity of the ‘susceptible community’, compared with the larger and broader community that listens to more common genres.
Highlights
Music is ubiquitous in society; everyone listens to it and most people prefer certain styles [1]
These results show that the SIR model performed very against cubic splines when fitted to songs as it did when fitted to epidemic curves
This study has explored the utility of a mechanistic epidemic model for describing song popularity by comparing its ability to capture song download patterns against that of a royalsocietypublishing.org/journal/rspa Proc
Summary
Music is ubiquitous in society; everyone listens to it and most people prefer certain styles [1]. If song popularity is driven by a contagious process, we would expect a mechanistic epidemic model to perform as well relative to a spline when the two models are applied to song download data as it does when they are applied to infectious disease data. If this is the case, we can attach meaning to the epidemiological parameters estimated for popular songs based on disease transmission model fits and interpret these parameters to draw mechanistic conclusions about song popularity, which we cannot do with purely phenomenological models like splines. We study data from a large and detailed database of song downloads from 2007 to 2014, a period when downloading (as opposed to streaming) was a primary method of music consumption (for further context on downloading versus streaming, see Aguiar [3])
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