A snapshot of change in popular music in the last decade: 2008 vs 2018

Among other memorable quotes on the 2018 anthem Rockstar, the modern day philosopher Post Malone quipped ‘Man I feel just like a rockstar’. Armed with a passion for data we decided to investigate: is rap really the new rock? Or the new pop? Is rock still relevant? What best describes typical pop lyrics? We explore chart-toppers of 2008 and 2018 with some visualizations.

The method:

The Results:

The Hot 100:

Rap has almost doubled its share of the top 100, while rock has fallen from a mere 12% in 2008 to 5% in 2018. In fact, we would argue that rap is now the dominant genre, even over pop (popular music, by definition). However, these charts are best viewed in conjunction with these scatterplots showing the spread of charting numbers across genres.

The scatters give an idea of exactly how popular the genres were in each year.

Looking at 2008’s plot, we can conclude that only a few rap songs cracked the top 40 – 6, to be precise — but pop had 20 hits in that range. Now after a decade, the number of rap hits in the top 40 has exploded to 20, and the number of pop songs in the top 40 has accordingly decreased to 16. Rock, the 4th most popular genre in 2008, had 16 songs in the hot 100; however, it was the least popular genre in 2018, contributing to only 5 of the top 100.


We thought another cool insight would be to compare the number of distinct words in songs (repetitions ignored). We knew rap would be the genre to reward repetition, but were amazed to find that in spite of having the most words by far, it — in both years — did not average the most number of unique words per song. In fact, in 2008, rap songs averaged over 100 unique words per song, but this dropped to around 80 in 2018 (we suspect because of the rise of the highly repetitive mumble rap, thanks Migos!)

Word Clouds:

This analysis is obviously far from perfect, but we had fun and think we pulled some interesting insights. If you are interested in learning how to web scrape or make some of these graphs, all our code and data is up on Github. Thank you for reading! Next up: we use Machine Learning to try and predict a song’s genre from its lyrics (with more data, obviously). Stay tuned!

A passion project that applies data science to some of our interests: music, sports and pop culture

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