Matthew Salganik gave a talk in the EE department with this title. He got his PhD in sociology last year under Duncan Watts, studying the (un)predictability of hits, blockbusters, best-sellers, etc. You probably read about it. If not, the basic idea is that they set up a website where people would come and listen to music and examined the influence of popularity on people’s listening habits. We’re not talking millions of songs here, just 48 chosen pretty much at random from unknown bands on PureVolume. Users could listen to any song and then after listening to it had the opportunity to download it.
As users arrived, they were assigned to one of eight completely separate “worlds”. In seven of these worlds, the users could see how many times each song had been downloaded, the last world served as a control in that users couldn’t see how popular each song was. The punchline is that in the worlds where people could influence each other, popular songs were downloaded a lot, but different songs became popular in each world. In the control group, some songs were still downloaded more than others, but the difference wasn’t as striking.
The graph from the talk that really stuck with me was this one, taken from their Science paper. It shows the marketshare of each song in the control world versus its marketshare in each of the seven influence worlds. The marketshare in the control world is taken as an un-influenced measure of quality, while the marketshare in the influence worlds are taken as measures of popularity. What you can see is a triangular shape indicating that the “bad” songs were unpopular in all worlds, while the “good” songs were only popular in some of the worlds. Sagalnik said that this agreed with what people in hit-based industries told them, that it’s easy to predict what won’t be a hit, but hard to predict what will.
