000189675 001__ 189675
000189675 005__ 20190316235721.0
000189675 0247_ $$2doi$$a10.1145/2512938.2512957
000189675 037__ $$aCONF
000189675 245__ $$aLaunch Hard or Go Home! Predicting the Success of Kickstarter Campaigns
000189675 269__ $$a2013
000189675 260__ $$bACM$$c2013
000189675 336__ $$aConference Papers
000189675 520__ $$aCrowdfunding websites such as Kickstarter are becoming increasingly popular, allowing project creators to raise hundreds of millions of dollars every year. However, only one out of two Kickstarter campaigns reaches its funding goal and is successful. It is therefore of prime importance, both for project creators and backers, to be able to know which campaigns are likely to succeed. We propose a method for predicting the success of Kickstarter campaigns by using both direct information and social features. We introduce a first set of predictors that uses the time series of money pledges to classify campaigns as probable success or failure and a second set that uses information gathered from tweets and Kickstarter's projects/backers graph. We show that even though the predictors that are based solely on the amount of money pledged reach a high accuracy, combining them with predictors using social features enables us to improve the performance significantly. In particular, only 4 hours after the launch of a campaign, the combined predictor reaches an accuracy of more than 76% (a relative improvement of 4%).
000189675 6531_ $$aCrowdfunding
000189675 6531_ $$aKickstarter
000189675 6531_ $$atime-series classification
000189675 6531_ $$asuccess prediction
000189675 6531_ $$asocial features
000189675 6531_ $$aTwitter
000189675 700__ $$0245633$$aEtter, Vincent$$g161149
000189675 700__ $$0241029$$aGrossglauser, Matthias$$g152655
000189675 700__ $$0240373$$aThiran, Patrick$$g103925
000189675 7112_ $$aThe first ACM conference on Online Social Networks (COSN'13)$$cBoston, Massachusetts, USA$$dOctober 7-8, 2013
000189675 773__ $$q177-182$$tProceedings of the first ACM conference on Online Social Networks (COSN'13)
000189675 8564_ $$s336495$$uhttps://infoscience.epfl.ch/record/189675/files/etter2013cosn.pdf$$yPreprint$$zPreprint
000189675 909C0 $$0252454$$pLCA3$$xU10431
000189675 909C0 $$0252455$$pLCA4$$xU10836
000189675 909CO $$ooai:infoscience.tind.io:189675$$pconf$$pIC$$qGLOBAL_SET
000189675 917Z8 $$x161149
000189675 917Z8 $$x161149
000189675 917Z8 $$x161149
000189675 937__ $$aEPFL-CONF-189675
000189675 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000189675 980__ $$aCONF