¶ Music Demographics, Accountability and Short Blasts of Finnish Metal · 27 April 2018 listen/tech
On 27 April 2018 I gave a talk at the 2018 Pop Conference called "Panic, Death and Other Gender Patterns in Spotify Listening".
The talk itself involved enough snapshots of changing data and short blasts of music that I am not going to attempt to transpose it directly to blog form, but the main point of it was to introduce some new things I have added to everynoise.com having to do with music and gender and numbers and genres and accountability and hope.
Demographics and Listening
The first of these things is an interactive index to the distinctive musical tastes of Spotify listeners divided by country/age/gender demographic groups. It is here:
Every Demographic at Once
This is similar in structure to Every Place at Once, which explores listening by city, and I retrofitted some new features from the demographic version to the geographic one, so both now try to describe each group's tastes in both genre and song terms.
The demographic groups include, where data allows, country/age groups who self-identify as nonbinary, which Spotify began offering as a gender choice in September 2016.
There's even a playlist of the music most distinctively popular among nonbinary listeners as a global group, which I played some songs from in the talk, and which you can hear here:
Streamshare
The second thing I discussed is an automatically-updating dataview breaking down the current share of Spotify streams in each country by artist-gender. That's here:
Female-Artist Streamshare by Country
It has data for each country overall, but also separately for "direct" listening (people going directly to album or artist pages and explicitly playing something), for Spotify's algorithmic personalized Discover Weekly playlists, and for Spotify's featured editorial playlists.
The initial state of this data was about like this:
- Per-country female-artist streamshare, by the most optimistic definition of "female artist" I could construct from my existing data, which is "music whose performers are not exclusively male", varies from about 16-33%.
- You might hope, or even expect, that listeners might seek out more female artists on their own than when they are just listening to other people's playlists or editorial programming, but in fact it's currently the reverse, and female streamshare of direct listening is consistently lower.
- Discover Weekly has notably lower female-artist streamshare than overall or direct listening. This is not very surprising, because DW is powered by co-reference in Spotify playlists, and globally male listeners make more and longer playlists than female listeners on average, so this is a pretty textbook example of algorithmic confirmation bias due to inherent asymmetries in the data inputs.
- Spotify editorial programming, on the other hand, actually has a significantly higher percentage of female-artist streams in general and in almost every country. (Which is an intentional state brought about and maintained by specific Spotify editorial effort.)
Genre Patterns
The third thing is a similar automatically-updating dataview of genres and their shares of both female listeners and streams from female or mixed-gender artists.
Gender Listening Patterns by Genre
There is a wide array of interesting individual differences across the genre spectrum, but the discouragingly unsurprising overall insight is that male listeners stream less from female artists than female listeners do, and in general even female listeners don't stream equally.
In an attempt to see what I could do to push against these inclinations, I took this genre and gender data and produced an experimental set of playlists that try to collect music by female or mixed-gender artists in every individual specific microgenre. The "genre" column in this table links to these, and initially about 1000 genres had enough artist-gender and genre-listening data to support playlists.
Obviously this is not the first time anybody has tried to make female-artist genre playlists, but to me these efforts too often use only a very reductive high-level notion of genre, like "Women in Pop" and "Women in Rock", and thus seem inherently tokenistic. This also tends to implicitly characterize the presence of women in music as a novelty and a separate subject from music itself. The converse ideal, I think, is for it to be true and well-understood that there are women making every kind of music, and for it to be possible to find and listen to filtered subsets of genre music by female or mixed-gender artists without this seeming token or novel. At best these lists would be amazing just because music made by humans is consistently and profoundly amazing.
In many genres, as you can trivially discover by exploring these lists, we are nowhere near this gender-balanced ideal. In some cases my robots couldn't find any plausible intersections between genre and gender at all, and in some of the playlists they did generate, the artists may be "mixed gender" on dubious technicalities, or the music may be more culturally adjacent to the genre than in it.
But the good news about this set of battles in the fight to undo the miserable legacies of chauvinism and patriarchy is that at worst you'll hear some additional music by men, which is sometime surprisingly decent, or you'll end up listening to some music that isn't exactly what you asked for, which is also not a terrible idea for life.
[The playlist I used for example snippets during the talk will make very little explanatory sense on its own, but it has music, and so I include it here for posterity, in case posterity runs out of music of its own.]
[5 June 2018: For anybody who wants a concise tl;dr instead of these detailed breakdowns, I added a summary.]
The talk itself involved enough snapshots of changing data and short blasts of music that I am not going to attempt to transpose it directly to blog form, but the main point of it was to introduce some new things I have added to everynoise.com having to do with music and gender and numbers and genres and accountability and hope.
Demographics and Listening
The first of these things is an interactive index to the distinctive musical tastes of Spotify listeners divided by country/age/gender demographic groups. It is here:
Every Demographic at Once
This is similar in structure to Every Place at Once, which explores listening by city, and I retrofitted some new features from the demographic version to the geographic one, so both now try to describe each group's tastes in both genre and song terms.
The demographic groups include, where data allows, country/age groups who self-identify as nonbinary, which Spotify began offering as a gender choice in September 2016.
There's even a playlist of the music most distinctively popular among nonbinary listeners as a global group, which I played some songs from in the talk, and which you can hear here:
Streamshare
The second thing I discussed is an automatically-updating dataview breaking down the current share of Spotify streams in each country by artist-gender. That's here:
Female-Artist Streamshare by Country
It has data for each country overall, but also separately for "direct" listening (people going directly to album or artist pages and explicitly playing something), for Spotify's algorithmic personalized Discover Weekly playlists, and for Spotify's featured editorial playlists.
The initial state of this data was about like this:
- Per-country female-artist streamshare, by the most optimistic definition of "female artist" I could construct from my existing data, which is "music whose performers are not exclusively male", varies from about 16-33%.
- You might hope, or even expect, that listeners might seek out more female artists on their own than when they are just listening to other people's playlists or editorial programming, but in fact it's currently the reverse, and female streamshare of direct listening is consistently lower.
- Discover Weekly has notably lower female-artist streamshare than overall or direct listening. This is not very surprising, because DW is powered by co-reference in Spotify playlists, and globally male listeners make more and longer playlists than female listeners on average, so this is a pretty textbook example of algorithmic confirmation bias due to inherent asymmetries in the data inputs.
- Spotify editorial programming, on the other hand, actually has a significantly higher percentage of female-artist streams in general and in almost every country. (Which is an intentional state brought about and maintained by specific Spotify editorial effort.)
Genre Patterns
The third thing is a similar automatically-updating dataview of genres and their shares of both female listeners and streams from female or mixed-gender artists.
Gender Listening Patterns by Genre
There is a wide array of interesting individual differences across the genre spectrum, but the discouragingly unsurprising overall insight is that male listeners stream less from female artists than female listeners do, and in general even female listeners don't stream equally.
In an attempt to see what I could do to push against these inclinations, I took this genre and gender data and produced an experimental set of playlists that try to collect music by female or mixed-gender artists in every individual specific microgenre. The "genre" column in this table links to these, and initially about 1000 genres had enough artist-gender and genre-listening data to support playlists.
Obviously this is not the first time anybody has tried to make female-artist genre playlists, but to me these efforts too often use only a very reductive high-level notion of genre, like "Women in Pop" and "Women in Rock", and thus seem inherently tokenistic. This also tends to implicitly characterize the presence of women in music as a novelty and a separate subject from music itself. The converse ideal, I think, is for it to be true and well-understood that there are women making every kind of music, and for it to be possible to find and listen to filtered subsets of genre music by female or mixed-gender artists without this seeming token or novel. At best these lists would be amazing just because music made by humans is consistently and profoundly amazing.
In many genres, as you can trivially discover by exploring these lists, we are nowhere near this gender-balanced ideal. In some cases my robots couldn't find any plausible intersections between genre and gender at all, and in some of the playlists they did generate, the artists may be "mixed gender" on dubious technicalities, or the music may be more culturally adjacent to the genre than in it.
But the good news about this set of battles in the fight to undo the miserable legacies of chauvinism and patriarchy is that at worst you'll hear some additional music by men, which is sometime surprisingly decent, or you'll end up listening to some music that isn't exactly what you asked for, which is also not a terrible idea for life.
[The playlist I used for example snippets during the talk will make very little explanatory sense on its own, but it has music, and so I include it here for posterity, in case posterity runs out of music of its own.]
[5 June 2018: For anybody who wants a concise tl;dr instead of these detailed breakdowns, I added a summary.]