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Comment by jonknee | Hacker News Reader

> To predict first weekend performance, they built a computer model, which factored in two variables: the rate of tweets around the release date and the number of theaters its released in.

Oh so this is to predict how well something will do after it's already doing it. The prediction markets work well before release. The only use for studios with this data is how widely to market/distribute the movie and any results from Twitter come after said marketing/distribution (and is likely actually a result of it).

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In the actual paper, they clearly state that their predictions for the opening weekend are based ONLY on the tweets that occur BEFORE the actual release. But I generally agree with you--prediction markets will be useful even months ahead of time, tweet rate is probably not.

They still seem to have created a model that fit the outcomes. There's no cross-validation here (since there's only two data points).

The real question is whether they can take this fitted model and apply it to future movies.

Go figure: by counting how many people are excited for a film the week before it opens, you can estimate how much money it makes. Even if the model is perfect and works everytime, it is not giving you any useful information.

If the prediction can be moved from one week out to a month or more out from the release of the film, there may be actual applications. For instance, helping theaters decide which movies to show and on how many screens, or how many weeks to run a movie for.

Totally agree with you -- I was just pointing out that the model in the article was never blind tested. It's not clear that it even works within its limited domain.

Problem is though that I doubt there would be much chatter that far out for a method like this to be overly useful. Chatter is usually the result of advertising with movies so being able to use it to predict things like what kind of advertising spend to use would be hard.

Even without practical information it is still a good demonstration of the type of information that can be extracted from twitter that would have been very hard previously online.

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