@inproceedings{63533a1416aa4a258f39ac6a66a09edd,
title = "Crowdfunding Support Tools: Predicting Success & Failure",
abstract = "Creative individuals increasingly rely on online crowdfunding platforms to crowdsource funding for new ventures. For novice crowdfunding project creators, however, there are few resources to turn to for assistance in the planning of crowdfunding projects. We are building a tool for novice project creators to get feedback on their project designs. One component of this tool is a comparison to existing projects. As such, we have applied a variety of machine learning classifiers to learn the concept of a successful online crowdfunding project at the time of project launch. Currently our classifier can predict with roughly 68% accuracy, whether a project will be successful or not. The classification results will eventually power a prediction segment of the proposed feedback tool. Future work involves turning the results of the machine learning algorithms into human-readable content and integrating this content into the feedback tool.",
keywords = "AdaBoost, Crowdfunding, Crowdsourcing, Kickstarter, Machine learning, Sentiment analysis",
author = "Greenberg, {Michael D.} and Bryan Pardo and Karthic Hariharan and Elizabeth Gerber",
year = "2013",
month = apr,
day = "27",
doi = "10.1145/2468356.2468682",
language = "English (US)",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "1815--1820",
editor = "Michel Beaudouin-Lafon and Patrick Baudisch and Mackay, {Wendy E.}",
booktitle = "CHI EA 2013 - Extended Abstracts on Human Factors in Computing Systems",
note = "31st Annual CHI Conference on Human Factors in Computing Systems:, CHI EA 2013 ; Conference date: 27-04-2013 Through 02-05-2013",
}