In 1985, the best selling song in the UK was Jennifer Rush’s The Power of Love. Thirty years later, it was Uptown Funk, by Mark Ronson, featuring Bruno Mars. From soft-rock power ballad to dance track, these were two very different chart toppers.
It’s obviously difficult to take a sample of two songs and draw sensible conclusions about changes in popular music. But what about a sample of 500,000 songs? That’s exactly what scientists at the University of California Irvine have done, to track trends in the success of different kinds of song between 1985 and 2015.
The researchers made use of the burgeoning availability of large datasets, in this case the crowd-sourced online music encyclopedias Musicbrainz and Acousticbrainz. They analysed half a million songs released in the UK during that 30-year period and correlated chart success with the acoustic features of the songs.
These are broken down into variables like timbre, tonality, danceability, mood and clusters of genres. The findings suggest there is a broad trend for fewer happy songs and more sad songs, while at the same time there has been an increase in the number of danceable songs. Yet while this kind of “big data” study can reveal new insights about what music people are listening to, it’s also important to look at the wider picture of how they listen.
The idea that pop songs are getting sadder makes for interesting reading and eye-catching headlines. But categorising songs as “happy” or “sad” also depends greatly on social context and interaction. Take the example of a song that topped the charts twice, 16 years apart, Queen’s Bohemian Rhapsody. It’s a complex multi-layered production, not straightforwardly danceable and sung from the perspective of a nihilistic murderer to whom “nothing really matters”. Yet it’s the source of much joyful group participation.
It’s also worth considering that the way we consume music, and how that consumption is measured, has changed a lot in 30 years. The charts are a lot less important now that the sheer amount of music available to the average listener is orders of magnitude greater than it was in 1985. Then, audiences relied on a comparatively small number of radio stations to hear new music. The charts were selected from a limited number of available singles and were much more prominent in people’s everyday listening.
Today, listeners have the history of recorded music in their pockets and increased control over how it’s playlisted and ordered to taste. The technology we use to listen to music has even altered our relationship with it, simultaneously expanding the parameters of musical choice and making the listening experience more intensely private.
Even though the charts themselves have adapted over the decades, incorporating downloads in 2004 and streaming in 2014, they no longer represent the same measure of cultural dominance they once did. As psychologists Raymond MacDonald, David Hargreaves and Dorothy Miell note, there has been a “democratisation of musical styles in that the previous association of certain styles with ‘seriousness’ and others with ‘popularity’ no longer exists to anything like the same extent”.
While the charts record mainstream success, they also interact with and are fed by musical subcultures that are often defined in opposition to that mainstream. They initially grow because they’re different to what’s in the charts but can eventually achieve success by building on that status, creating tensions with the original fans.
For example, once tabloid newspapers began regularly using terms like “acid house” and featuring smiley face t-shirts in their fashion selections, many original rave fans moved onto maintain their sense of distance and opposition from the mainstream. It’s a familiar pattern with musical subcultures – from mods, to hippies to punks – as their markers of difference become incorporated into the wider cultural milieu.
Popular music, then, is contested territory. Patterns of taste are in constant flux, with chart success being only one axis of music’s impact.
The limits of big data
The recommendation algorithms of large tech companies are increasingly a part of the process of musical and cultural choice, and the massive datasets associated with this are a huge resource for researchers. But the “popular” in popular music is more than just a quantitative measure of consumption, and we can’t just reduce it to aesthetic and stylistic components. We also need to take account of its social functions. And that means researchers from different disciplines – both arts and science – engaging in dialogue to analyse and interpret the data.
Music encoded as digital data is now feeding into the broader matrix of economic and political decision making, such as the Bank of England using it to take the economic temperature. So it’s more important than ever that the social aspect of music’s use does not get buried beneath the numbers.