Posts Tagged gender
Gender Specific Listening
Posted by Paul in data, Music, music information retrieval, recommendation, research, The Echo Nest, zero ui on February 10, 2014
One of the challenges faced by a music streaming service is to figure out what music to play for the brand-new listener. The first listening experience of a new listener can be critical to gaining that listener as a long time subscriber. However, figuring out what to play for that new listener is very difficult because often there’s absolutely no data available about what kind of music that listener likes. Some music services will interview the new listener to get an idea of their music tastes.
However, we’ve seen that for many listeners, especially the casual and indifferent listeners, this type of enrollment may be too complicated. Some listeners don’t know or care about the differences between Blues, R&B and Americana and thus won’t be able to tell you which they prefer. A listener whose only experience in starting a listening session is to turn on the radio may not be ready for a multi-screen interview about their music taste.
So what can a music service play for a listener when they have absolutely no data about that listener? A good place to start is to play music by the most popular artists. Given no other data, playing what’s popular is better than nothing. But perhaps we can do better than that. The key is in looking at the little bit of data that a new listener will give you.
For most music services, there’s a short user enrollment process that gets some basic info from the listener including their email address and some basic demographic information. Here’s the enrollment box for Spotify:
Included in this information is the date of birth and the gender of the listener. Perhaps we can use basic demographic data to generate a slightly more refined set of artists. For starters, lets consider gender. Let’s try to answer the question: If we know that a listener is male or female does that increase our understanding of what kind of music they might like? Let’s take a look.
Exploring Gender Differences in Listening
Do men listen to different music than women do? Anecdotally, we can think of lots of examples that point to yes – it seems like more of One Direction’s fans are female, while more heavy metal fans are male, but lets take a look at some data to see if this is really the case.
The Data – For this study, I looked at the recent listening of about 200 thousand randomly selected listeners that have self-identified as either male or female. From this set of listeners, I tallied up the number of male and female listeners for each artist and then simply ranked the artists in order or listeners. Here’s a quick look at the top 5 artists by gender.
Top 5 artists by gender
|2||Bruno Mars||Daft Punk||Bruno Mars|
|4||Katy Perry||Bruno Mars||Katy Perry|
Among the top 5 we see that the Male and Female listeners only share one artist in common:Bruno Mars. This trend continues as we look at the top 40 artists. Comparing lists by eye can be a bit difficult, so I created a slopegraph visualization to make it easier to compare. Click on this image to see the whole slopegraph:
Looking at the top 40 charts artists we see that more than a quarter of the artists are gender specific. Artists that top the female listener chart but are missing on the male listener chart include: Justin Bieber, Demi Lovato, Shakira, Britney Spears, One Direction, Christina Aguilera, Ke$ha, Ciara, Jennifer Lopez, Avril Lavigne and Nicki Minaj. Conversely, artists that top the male listener chart but are missing on the top 40 female listener chart include: Bob Marley, Kendrick Lamar, Wiz Khalifa, Avicii, T.I. Queen, J.Cole, Linkin Park, Kid Cudi and 50 Cent. While some artists seem to more easily cross gender lines like Rihanna, Justin Timberlake, Lana Del Rey and Robin Thicke.
No matter what size chart we look at – whether it is the top 40, top 200 or the top 1000 artists – about 30% of artists on a gender-specific chart don’t appear on the corresponding chart for the opposite gender. Similarly, about 15% of the artists that appear on a general chart of top artists will be of low relevance to a typical listener based on these gender-listening differences.
What does this all mean? If you don’t know anything about a listener except for their gender, you can reduce the listener WTFs by 15% for a typical listener by restricting plays to artists from the gender specific charts. But perhaps even more importantly, we can use this data to improve the listening experience for a listener even if we don’t know a listener’s gender at all. Looking at the data we see that there are a number of gender-polarizing artists on any chart. These are artists that are extremely popular for one gender, but not popular at all for the other. Chances are that if you play one of these polarizing artists for a listener that you know absolutely nothing about, 50% of the time you will get it wrong. Play One Direction and 50% of the time the listener won’t like it, just because 50% of the time the listener is male. This means that we can improve the listening experience for a listener, even if we don’t know their gender by eliminating the gender skewing artists and replacing them with more gender neutral artists.
Let’s see how this would affect our charts. Here are the new Top 40 artists when we account for gender differences.
|16||23||Lana Del Rey|
|19||27||The Black Eyed Peas|
|24||22||Macklemore & Ryan Lewis|
Artists promoted to the chart due to replace gender-skewed artists are in bold. Artists that were dropped from the top 40 are:
- Avicii – skews male
- Justin Bieber – skews female
- Christina Aguilera – skews female
- One Direction – skews female
- Demi Lovato – skews female
Who are the most gender skewed artists?
The Top 40 is a fairly narrow slice of music. It is much more interesting to look at how listening can skew across a much broader range of music. Here I look at the top 1,000 artists listened to by males and the top 1,000 artists listened to by females and find the artists that have the largest change in rank as they move from the male chart to the female chart. Artists that lose the most rank are artists that skew male the most, while artists that gain the most rank skew female.
Top male-skewed artists:
artists that skew towards male fans
- Iron Maiden
- Rage Against the Machine
- Van Halen
- Jimi Hendrix
- Limp Bizkit
- Wu-Tang Clan
- The Who
- Alice in Chains
- Black Sabbath
- Stone Temple Pilots
- Mobb Deep
- Queens of the Stone Age
- Ice Cube
Top female-skewed artists:
artists that skew towards female fans
- Danity Kane
- Cody Simpson
- Hannah Montana
- Emily Osment
- Playa LImbo
- Vanessa Hudgens
- Miranda Lambert
- Aly & AJ
- Christina Milian
- Noel Schajris
- Maria José
- Jesse McCartney
- Bridgit Mendler
- Luis Fonsi
- La Oreja de Van Gogh
- Michelle Williams
- Lindsay Lohan
By looking at the genres of the most gender skewed artists we can also get a sense of which genres are most gender skewed as well. Looking at the genres of the top 1000 artists listened to by male listeners and the top 1000 artists with female listeners we identify the most skewed genres:
Genres most skewed to female listeners:
- Dance Pop
- Contemporary Hit Radio
- Urban Contemporary
- Hot Adult Contemporary
- Latin Pop
- Teen Pop
- Neo soul
- Pop rock
- Contemporary country
Genres most skewed to male listeners:
- Hip Hop
- Album Rock
- Pop Rap
- Indie Rock
- Funk Rock
- Gangster Rap
- Electro house
- Classic rock
- Nu metal
This study confirms what we expected – that there are differences in gender listening. For mainstream listening about 30% of the artists in a typical male’s listening rotation won’t be found in a typical female listening rotation and vice versa. If we happen to know a listener’s gender and nothing else, we can improve their listening experience somewhat by replacing artists that skew to the opposite gender with more neutral artists. We can even improve the listening experience for a listener that we know absolutely nothing about – not even their gender – by replacing gender-polarized artists with artists that are more accepted by both genders.
Of course when we talk about gender differences in listening, we are talking about probabilities and statistics averaged over a large number of people. Yes, the typical One Direction fan is female, but that doesn’t mean that all One Direction fans are female. We can use gender to help us improve the listening experience for a brand new user, even if we don’t know the gender of that new user. But I suspect the benefits of using gender for music scheduling is limited to helping with the cold start problem. After a new user has listened to a dozen or so songs, we’ll have a much richer picture of the type of music they listen to – and we may discover that the new male listener really does like to listen to One Direction and Justin Bieber and that new female listener is a big classic rock fan that especially likes Jimi Hendrix.
update – 2/13 – commenter AW suggested that the word ‘bias’ was too loaded a term. I agree and have changed the post replacing ‘bias’ with ‘difference’
PAPPA – Paul’s Awesome Party Playlisting App
Posted by Paul in code, data, events, fun, hacking, Music, music hack day, playlist, The Echo Nest on November 11, 2013
For my Boston Music Hack Day hack I built Yet Another Party Playlisting App (YAPPA), because the world needed another party playlister – but really, I built it because I needed another hack, because 15 hours into the 24 hour hackathon I realized that my first hack just wasn’t going to work (more on that in another post). And so, with 9 hours left in the hack day, I thought I would try my hand at the party playlisting app.
The YAPPA is a frequently built app. In some sense one can look at the act of building a YAPPA as a hacking exercise. Just as a still life painter will practice by painting a bowl of fruit, or a pianist will practice scales, a music hacker can build their hacking muscle by creating a YAPPA.
The essential features of a YAPPA are straightforward – create a listening experience for a party based upon the tastes of the guests. Allow guests to suggest music for the party, apply some rules to select music that satisfies all the guests, and keep the music flowing.
With those features in mind, I created my party playlisting app. The interface is dead simple – guests can add music to the party via the master web interface or text the artist and song from the mobile phones to the party phone number. Once the party has started, PAPPA will keep the music flowing.
The key technology of PAPPA is how it picks the music to play next. Most YAPPAs will try to schedule music based on fairness so that everyone’s music taste is considered. Some YAPPAs also use song attributes such as song hotttnesss, song energy and danceability to make sure that the music matches the vibe of the party. PAPPA takes a very different approach to scheduling music. That’s because PAPPA takes a very different approach to parties. PAPPA doesn’t like parties. PAPPA wants everyone to go home. So PAPPA takes all of these songs that have been carefully texted to the party phone number, along with all the artist and song suggestions submitted via the web and throws them away. It doesn’t care about the music taste of the guests at the party. In fact it despises their taste (and the guests as well). Instead, PAPPA selects and plays the absolute worst music it can find. It gives the listener an endless string of the most horrible (but popular) music. Here’s a sample (the first 3 songs are bait to lure in the unwitting party guests):
- Royals by Lorde
- Levels by Avicii
- Blurred Lines by Robin Thicke
- #Twerkit featuring Nicki Minaj by Busta Rhymes
- From The Bottom Of My Broken Heart by Britney Spears
- Amigas Cheetahs by The Cheetah Girls
- Do Ya Think I’m Sexy by Paris Hilton
- Incredible by Clique Girlz
- No Ordinary Love by Jennifer Love Hewitt
- Mexican Wrestler by Emma Roberts
- I Don’t Think About It by Emily Osment
- A La Nanita Nana by The Cheetah Girls
- Don”t Let Me Be The Last To Know by Britney Spears
- Wild featuring Big Sean by Jessie J
- Heartbeat (Album Version) by Paris Hilton
- Love The Way You Love Me by The Pussycat Dolls
- When You Told Me You Loved Me by Jessica Simpson
- Jericho by Hilary Duff
- Strip by Brooke Hogan
- Pero Me Acuerdo De Tí by Christina Aguilera
- Bang Bang by Joachim Garraud
- Right Now featuring David Guetta (Sick Individuals Dub) by Rihanna
- Wilde Piraten by The Cool Kids
- Friend Lover by Electrik Red
- Betcha Can’t Do It Like Me by D4L
- Who’s That Girl by Hilary Duff
- Get In There, Frank! by Fun
- Hold It Don”t Drop It by Jennifer Lopez
- Sweet Sixteen by Hilary Duff
- Live It Up featuring Pitbull by Jennifer Lopez
- Freckles by Natasha Bedingfield
- I Want You by Paris Hilton
- Hold It Close by Fun
- Magic by The Pussycat Dolls
- How To Lose A Girl by Mitchel Musso
- Fairy Tales by JoJo
- Slow It Down featuring Fabolous (Album Version (Explicit)) by The-Dream
- Mr. Hamudah by Charles Hamilton
- Promise by Vanessa Hudgens
- Metamorphosis by Hilary Duff
How does PAPPA find the worst music in the world? It looks through all the data that The Echo Nest is collecting about how people experience music online to find the songs that have been banned frequently. When a music listener says “ban this song” they are making a pretty strong statement about the song – essentially saying, “I do not ever want to hear that song again in my life”. PAPPA finds these songs that have the highest banned-to-play ratio (i.e. the songs that have been proportionally banned the most when play count is taken into consideration) and adds them to the playlist. The result being a playlist filled with the most reviled music – with songs by Paris Hilton, Jennifer Love Hewitt and the great Emma Roberts. The perfect playlist to send your guests home.
At this moment, lets pause and listen to the song Mexican Wrestler by Emma Roberts:
What happens to all those carefully crafted text messages of songs sent by the guests? No, there’s no Twilio app catching all those messages, parsing out songs and adding them to a play queue to be scheduled. They just go to my phone. That’s so if people are not leaving the party fast enough, I can use all the phone numbers of the guests to start to text them back and tell them they should go home.
By the way, if you look at the songs that were texted to me during my two minute demo you’d realize how fruitless a YAPPA really is. There’s no possible way to make a party playlist that is going to satisfy everyone in the room. Tastes are too varied, and there’s always that guy who thinks he is clever by adding some Rick Astly to the party queue. Here’s what was texted to me during my two minute demo:
- Gregory Porter – be good
- Rebecca Black – It’s Friday
- Weird Al Yankovic – Fat
- Lady Gaga – Applause
- Weird Al Yankovic – Amish Paradise (from a different phone number from the other weird Al fan)
- boss ass bitch
- Basement Jaxx raindrops
- John Mayer your body is a wonderland
- jay z holy grail
- Underworld spikee
- wake me up
- Britney Spears – Hit Me Baby One More Time
- Slayer War Ensemble
- Bieber baby
- Ra Ra riot
- Rick Astley
- Mikey Cyrus
- Hi paul
- Stevie wonder overjoyed
Imagine trying to build a party playlist based upon those 24 input songs. Admittedly, a hackathon demo session is not a real test case for a party playlister but I still think you’d end up with a terrible mix of songs that no smart algorithm, nor any smart human, could stitch together into a playlist that would be appropriate and pleasing for a party. My guess is that if you did an A/B test for two parties, where one party played music based upon suggestions texted to a YAPPA and the other party played the top hotttest songs, the YAPPA party would always lose. I’d run this test, but that would mean I’d have to go to two parties. I hate parties, so this test will never happen. Its one of the flaws in our scientific method.
Who are the worst artists?
Looking at the PAPPA playlists I see a number of recurring artists – Britney Spears and Paris Hilton seem to be well represented. I thought it would be interesting to create a histogram of the top recurring artists in the most banned songs list. Here’s the fascinating result:
One thing I find notable about this list is the predominance of female artists. Females outnumber males by a substantial amount. Here’s some pie:
80% of the most banned artists are female. A stunning result. There’s something going on here. Someone suggested that the act of banning a song is an aggressive act that may skew male, and many of these aggressively banning males don’t like to listen to female artists. More study is needed here. It may involve parties, so I’m out.
Wrapping it all up
I enjoyed creating my PAPPA YAPPA. Demoing it was really fun and the audience seemed to enjoy the twist ending. The patterns in the data underlying the app are pretty interesting too. Why are so many banned songs by female artists?
If you are having your own party and want to use PAPPA to help enhance the party you can go to:
Just replace the phone number in the URL with your own and you are good to go.