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Project Success Prediction in Crowdfunding Environments

Authors:

Vineeth Rakesh,

Chandan K. ReddyAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 247 - 256

https://doi.org/10.1145/2835776.2835791

Published: 08 February 2016 Publication History

Abstract

Crowdfunding has gained widespread attention in recent years. Despite the huge success of crowdfunding platforms, the percentage of projects that succeed in achieving their desired goal amount is only around 40%. Moreover, many of these crowdfunding platforms follow "all-or-nothing" policy which means the pledged amount is collected only if the goal is reached within a certain predefined time duration. Hence, estimating the probability of success for a project is one of the most important research challenges in the crowdfunding domain. To predict the project success, there is a need for new prediction models that can potentially combine the power of both classification (which incorporate both successful and failed projects) and regression (for estimating the time for success). In this paper, we formulate the project success prediction as a survival analysis problem and apply the censored regression approach where one can perform regression in the presence of partial information. We rigorously study the project success time distribution of crowdfunding data and show that the logistic and log-logistic distributions are a natural choice for learning from such data. We investigate various censored regression models using comprehensive data of 18K Kickstarter (a popular crowdfunding platform) projects and 116K corresponding tweets collected from Twitter. We show that the models that take complete advantage of both the successful and failed projects during the training phase will perform significantly better at predicting the success of future projects compared to the ones that only use the successful projects. We provide a rigorous evaluation on many sets of relevant features and show that adding few temporal features that are obtained at the project's early stages can dramatically improve the performance.

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Cited By

Bambi JOlobatuyi KSantoso YSadri HMoselle KRudnick ADong GChang EKuo A(2024)Use of Patterns of Service Utilization and Hierarchical Survival Analysis in Planning and Providing Care for Overdose Patients and Predicting the Time-to-Second OverdoseKnowledge10.3390/knowledge40300244:3(444-461)Online publication date: 19-Aug-2024
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Wang TJin FHu YFeng LCheng Y(2024)Making Early and Accurate Deep Learning Predictions to Help Disadvantaged Individuals in Medical CrowdfundingProduction and Operations Management10.1177/10591478241231846Online publication date: 1-Mar-2024
https://doi.org/10.1177/10591478241231846
ForouzeshNejad AArabikhan FWilliams NGegov ASari OBader M(2024)Agile Project Status Prediction Using Interpretable Machine Learning2024 IEEE 12th International Conference on Intelligent Systems (IS)10.1109/IS61756.2024.10705197(1-8)Online publication date: 29-Aug-2024
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Index Terms

Project Success Prediction in Crowdfunding Environments
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

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Published: 08 February 2016

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National Science Foundation

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WSDM 2016

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Bambi JOlobatuyi KSantoso YSadri HMoselle KRudnick ADong GChang EKuo A(2024)Use of Patterns of Service Utilization and Hierarchical Survival Analysis in Planning and Providing Care for Overdose Patients and Predicting the Time-to-Second OverdoseKnowledge10.3390/knowledge40300244:3(444-461)Online publication date: 19-Aug-2024
https://doi.org/10.3390/knowledge4030024
Wang TJin FHu YFeng LCheng Y(2024)Making Early and Accurate Deep Learning Predictions to Help Disadvantaged Individuals in Medical CrowdfundingProduction and Operations Management10.1177/10591478241231846Online publication date: 1-Mar-2024
https://doi.org/10.1177/10591478241231846
ForouzeshNejad AArabikhan FWilliams NGegov ASari OBader M(2024)Agile Project Status Prediction Using Interpretable Machine Learning2024 IEEE 12th International Conference on Intelligent Systems (IS)10.1109/IS61756.2024.10705197(1-8)Online publication date: 29-Aug-2024
https://doi.org/10.1109/IS61756.2024.10705197
Cai ZDing HXu MCui X(2024)Multimodal dynamic graph convolutional network for crowdfunding success predictionApplied Soft Computing10.1016/j.asoc.2024.111313154(111313)Online publication date: Mar-2024
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Kilinc MAydin C(2023)Feature selection for Turkish Crowdfunding projects with using filtering and wrapping methodsElectronic Commerce Research and Applications10.1016/j.elerap.2023.101340(101340)Online publication date: Dec-2023
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