

Predicting Kickstarter campaigns success within 4 hours of launch [pdf] - lum
http://vincent.etter.io/publications/etter2013cosn.pdf

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Wookai
I'm the main author of the paper, and creator of
[http://sidekick.epfl.ch/](http://sidekick.epfl.ch/). If you have any
question, please ask, I'll be happy to answer!

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eurekin
I didn't see any classification measures besides accuracy... Could You provide
the confusion matrix? It's easy to derive all the important values (FPR, TPR,
precision so on) and get a lot better idea of quality.

Since the outcome is either success or not, the coin flip classifier yields an
accuracy of 50%. It would be worth noting that in the paper.

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Wookai
It's true that that should have mentionned it in the paper. The basline here
is always telling it fails, which gives you about 52% accuracy. I should
definitely provide the confusion matrix, it's on my TODO list. I will add a
performance section to the website where I show this kind of information.

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eurekin
Yes, thank you.

I didn't mean to put down the article. Quite the opposite. It's truly an
amazing application and a well thought through execution of the machine
learning methods.

I have been wondering for some time, what the supervised learning could be
useful for, which could benefit the broader audience (beyond computer vision,
gaming, medicine and all the much popular and repeated applications). That You
nailed perfectly.

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Wookai
No problem, I'm glad to get feedback, I didn't take it negatively ;).

Thanks a lot for your kind comments!

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muzicmogul
Vincetn, What's the best way to contact you about the paper. I sent you an
email today to your EPFL email. Thanks, Robson.

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Wookai
Robson, I got your email, thanks. I just answered. Cheers!

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rst
One possible explanation: the initial boost is largely a reflection of the
pre-launch marketing efforts, which are in turn a measure of the effectiveness
of the marketing effort generally. So, you could read this as saying more that
marketing is important, than that there's something that's special in
particular about the large "pop".

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Wookai
Sure, many factors can explain the initial "surge" in pledges: pre-launch
marketing, featuring on recently launched, novelty, first spread on social
medias, etc. I did not really try to explain the causes of the different
phases, but merely to use the raw data to predict the state at the end
(successful or failed).

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matthewarnold
From your findings, is it possible to create a sentence formulated like this?
"If, after X hours, your KS has $X and X links, your chance of success is X in
X." If not, can you create a comparable "headline fact"?

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Wookai
I haven't done it yet, but I could. This also depends on what your goal is,
and how long the campaign last, but it is quite easy to extract such
probabilities of success. For instance, this plot shows the estimated
probability of success as a function of time and the proportion of the goal
that was pledged: [http://imgur.com/hoTzBEW](http://imgur.com/hoTzBEW). From
it, you can see that you need to be at about 25% or more of your goal in the
middle for your campaign if you want to have more chances of success than
failure. Similar plots could be done by including more factors.

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sfrechtling
What I found curious was the difference in goals for successful and non-
successful projects. It seemed that lower goals had a higher chance of
succeeding (all things considered). Would that be a right conclusion to jump
to?

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Wookai
It is correct: on average, failed projects ask for more money than successful
ones, _except_ for projects in the Video Games category. There, it is the
opposite. I'll add a "Stats" page to the website as soon as possible to show
these results.

