Download patterns of catchy music resemble infectious diseases spread curves, says study

The download pattern for catchy songs resembles the curves drawn by epidemiologists to chart the spread of infectious diseases, according to a new study.

Mathematicians, including Dora P Rosati from McMaster University in Canada, say the social processes underlying song popularity are similar to those that drive the jumping of infectious diseases from one host to another.

They say the findings, published in the journal Proceedings of the Royal Society A: Mathematical and Physical Sciences, could help build better tools to study song popularity.

In the research, the scientists analysed data from the online music streaming service MixRadio, comprising information on song downloads via Nokia cell phones in the UK from 2007 to 2014.

They then assessed the ability of what is known as the SIR (susceptible–infectious–recovered) model – used by epidemiologists to chart the spread of diseases – to plot the download pattern of popular songs, including Bad Romance by Lady Gaga, and The A Team by Ed Sheeran.

When an infectious disease, such as Covid-19, enters a population, it is initially transmitted from person to person via social interactions.

The prevalence of the disease eventually peaks, following which the disease spread declines as the number of susceptible people in the population declining, and/or as infected individuals recover.

Epidemiologists apply this understanding to forecast the spread of diseases using the SIR mathematical model.

Similarly, when a new hit song is released, the scientists say it also “spreads” rapidly through a population, “from person to person and through various media, eventually reaching some peak popularity and then diminishing in appeal.”

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“At the end of a disease epidemic, a large proportion of the population will have been infected with the disease, whereas at the end of a hit song’s period of extreme popularity, a large proportion of the population will recognise that song,” the researchers note in the study, drawing comparison between the two processes.

The mathematicians found that the SIR model describes song download trends for popular songs well, and could be a good representation of the processes driving song popularity.

The analysis also revealed that the pattern of downloads differs for songs depending on their genre.

For instance, the download pattern for Electronica songs in the music database followed a shorter time period than that of Pop songs, according to the study.

Songs in the Electronica genre, the scientists say, appear to gain popularity faster than those in other genres, and “burn through their susceptible populations more quickly.”

The study defined susceptible population as the group of individuals who may download a song if exposed to it.

Comparing the way songs from the two genres were shared and downloaded, the mathematicians speculate that fans of Electronica may likely be more efficient or more active at transmitting their favourite songs more actively.

“The social network of Electronica fans might be more strongly connected than fan communities of other music genres such as Pop. Electronica fans may be more passionate about their favourite songs and bands than Pop fans, and therefore talk about and promote their favourite songs more,” they wrote in the research.

“Popular songs are often described as ‘viral’ or ‘catchy’ as if they could ‘infect’ people, perhaps this description is more apt than has been previously recognised,” they added.

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The researchers believe the SIR model can be used to capture the underlying “song transmission mechanism or the contagious process that drives song popularity.”

To validate the findings, the scientists call for future studies with the same type of analysis on a different song dataset.

“Although our results show that the model describes song download dynamics well, it has too little structure to represent all the nuances of a song spreading through a population,” the study noted.

The scientists say studying song download patterns using more nuanced disease spread models, which have more biological insights, may shed better light on important processes driving “song transmission”.


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