read TechCrunch and spend a lot of time online. They are much more familiar with how you use this kind of software. They are contributor types. Later on in the process, as the topics get built out and people are finding Hunch through search engines and coming at it from a different direction, you will have a less participatory type of crowd who are more consumers than contributors. When Flickr was acquired by Yahoo, we had about a quarter of a million users, and 20 percent of the users were creating 80 percent of the content. At Wikipedia, there is no longer any need to build out the articles about gazelles or orang-utans. So we anticipate that the contribution percentage at Hunch will slow down.
On the other hand, there’s no limit to human questioning. There are a lot of people who need to make the same decisions—where should I go to college, what video camera should I buy, how should i get my hair cut—but there are also lots of questions that I didn’t even know people had. For example, some really wonderful things have shown up in Hunch in the past 24 hours, one of which was the question, “Which monster truck should I root for?” I knew monster trucks existed, but somebody built out this fantastic topic where they listed all the types of monster trucks, and the teams, and paint styles. Somebody else made another topic—what pen-and-pencil game should I play? Before that, I could only name two or three, tic-tac-toe, hangman, and pen-the-pig. So there’s a topic I would never have though of. That’s one of the things that is wonderful about building user-generated content sites. There is no end of interesting things that people will add to the system.
X: That’s true, but doesn’t it actually take a bit of skill and practice to come up with a topic where the decision tree includes good questions, and leads to useful outcomes? One of the problems I noticed with Hunch when I was reviewing it back in April was that there wasn’t much guidance about how to create good topics.
CF: This is exactly the kind of thing we’re working on codifying. There are already some very successful topics—for example, “Which hotel should I stay at in London?” It starts out by asking whether you want to stay in this or that neighborhood, how much you’re looking to pay, do you want something that’s fancy and decorated or more contemporary, a boutique or a bit hotel, et cetera. That’s a very successful topic, because it gives people answers that they say they like. We should be able to replicate the best practices for the “hotels in London” topic for hotels in Dallas, Paris, Tokyo, et cetera. That’s something we’re working on, but we haven’t actually come up with a design for how you present this as a best practice to someone who creates a topic. This is something we’re acutely aware of.
X: What else goes into ensuring a positive outcome for users?
CF: There are a bunch of different algorithms and machine-learning aspects to this problem. For example, there is a question selection algorithm that figures out which questions most frequently lead to a positive outcome. For example, I was helping to write a topic on “What young adult book should I read?” It had all these recommendations like The Red Pony and The Black Pearl and Of Mice and Men and Lord of the Flies and all these things you read when you’re a teenager. One of the questions that had been asked by the original question-maker—and this is not to embarrass this person—but the question was, “Have you ever read a young adult novel before?” Which is kind of a lame question for this topic, because the answers were either, “Yes, I have,” or “No, but I’m willing to try.” So what will end up happening is that the algorithm will discover over time that this is not a very predictive question. People answer yes or no, but it doesn’t actually contribute to a positive outcome. So the algorithm will mothball that question after a period of time.
Then there’s also this body of “Teach Hunch About You” questions, like “Do you believe in angels?,” which are fun and, interestingly, very predictive. So we also try to figure out, when we’re giving replies, how “taste-oriented” the question is. An example would be, if I’m a punk-rock chick hanging out in Harvard Square, or if I’m a housewife in suburban Connecticut, I might own the same TV either way and it wouldn’t have much to do with my taste. On the other hand, punk rock chick would probably
