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Launch hard or go home!: predicting the success of kickstarter campaigns

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Published:07 October 2013Publication History

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%).

References

  1. Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20(3):273--297, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Thomas Cover and Peter Hart. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1):21--27, 1967. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Michael D Greenberg, Bryan Pardo, Karthic Hariharan, and Elizabeth Gerber. Crowdfunding support tools: predicting success & failure. In CHI'13 Extended Abstracts on Human Factors in Computing Systems, pages 1815--1820. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Ethan Mollick. The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing, 2013.Google ScholarGoogle Scholar
  5. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12:2825--2830, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Twitter Developers. Frequently asked questions. Retreived August 24, 2013 from https://dev.twitter.com/docs/faq#6861.Google ScholarGoogle Scholar
  7. Rick Wash. The value of completing crowdfunding projects. In ICWSM'13: 7th International AAAI Conference on Weblogs and Social Media, 2013.Google ScholarGoogle Scholar

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  1. Launch hard or go home!: predicting the success of kickstarter campaigns

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    • Published in

      cover image ACM Conferences
      COSN '13: Proceedings of the first ACM conference on Online social networks
      October 2013
      254 pages
      ISBN:9781450320849
      DOI:10.1145/2512938

      Copyright © 2013 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 October 2013

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      Acceptance Rates

      COSN '13 Paper Acceptance Rate22of138submissions,16%Overall Acceptance Rate69of307submissions,22%

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