Name
Abstract | ||
|---|---|---|
Crowdfunding websites such as Kickstarter are becoming increasingly popular, allowing project creators to raise hundreds of millions of dollars every year. However, only one out of two Kickstarter campaigns reaches its funding goal and is successful. It is therefore of prime importance, both for project creators and backers, to be able to know which campaigns are likely to succeed. We propose a method for predicting the success of Kickstarter campaigns by using both direct information and social features. We introduce a first set of predictors that uses the time series of money pledges to classify campaigns as probable success or failure and a second set that uses information gathered from tweets and Kickstarter's projects/backers graph. We show that even though the predictors that are based solely on the amount of money pledged reach a high accuracy, combining them with predictors using social features enables us to improve the performance significantly. In particular, only 4 hours after the launch of a campaign, the combined predictor reaches an accuracy of more than 76% (a relative improvement of 4%). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2512938.2512957 | COSN |
Keywords | Field | DocType |
probable success,backers graph,crowdfunding web,kickstarter campaign,combined predictor,social feature,project creator,direct information,money pledge,high accuracy | Prime (order theory),Graph,Computer security,Computer science,Time series classification | Conference |
Citations | PageRank | References |
32 | 1.54 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincent Etter | 1 | 51 | 3.06 |
| M. Grossglauser | 2 | 2965 | 448.13 |
| Patrick Thiran | 3 | 2712 | 217.24 |