Prediction of Success in Crowdfunding Platforms

Partha Mukherjee, Youakim Badr, Srushti N. Karvekar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Membership platforms serve as a source of constant earning to the independent creators who create media content such as images, videos, podcasts that are patronized by creators' followers. This mechanism leads to build the platform where the patrons contribute to raise funds to promote the creator. Patreon is one of the largest membership-based platforms that crowdfunds the media content-based projects. Predicting the success of crowdfunding projects is equally important for projects' creators and investors. In this research we resort to supervised machine learning techniques to provide decision-making supports for prediction of success or failure of such project. By comparing Naïve Bayes, Logistic regression and Random Forest classifiers we demonstrate that Random Forest classifier with an accuracy of 71.5% outperforms the other two classifiers in success prediction. The findings will help the creators to better decide on their projects and improve their fan/follower base using different social media platforms.

Original languageEnglish (US)
Title of host publication2020 International Conference on Decision Aid Sciences and Application, DASA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages233-237
Number of pages5
ISBN (Electronic)9781728196770
DOIs
StatePublished - Nov 8 2020
Event2020 International Conference on Decision Aid Sciences and Application, DASA 2020 - Virtual, Sakheer, Bahrain
Duration: Nov 7 2020Nov 9 2020

Publication series

Name2020 International Conference on Decision Aid Sciences and Application, DASA 2020

Conference

Conference2020 International Conference on Decision Aid Sciences and Application, DASA 2020
CountryBahrain
CityVirtual, Sakheer
Period11/7/2011/9/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Control and Optimization

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