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http://dx.doi.org/10.9717/kmms.2018.21.2.281

Predicting Success of Crowdfunding Campaigns using Multimedia and Linguistic Features  

Lee, Kang-hee (Department of Software., Sangmyung university)
Lee, Seung-hun (Department of Software., Sangmyung university)
Kim, Hyun-chul (Department of Software., Sangmyung university)
Publication Information
Journal of Korea Multimedia Society / v.21, no.2, 2018 , pp. 281-288 More about this Journal
Abstract
Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging startup companies in recent years. Despite the huge success of crowdfunding, it has been reported that only around 40% of crowdfunding campaigns successfully raise the desired goal amount. The purpose of this study is to investigate key factors influencing successful fundraising on crowdfunding platforms. To this end, we mainly focus on contents of project campaigns, particularly their linguistic cues as well as multiple features extracted from project information and multimedia contents. We reveal which of these features are useful for predicting success of crowdfunding campaigns, and then build a predictive model based on those selected features. Our experimental results demonstrate that the built model predicts the success or failure of a crowdfunding campaign with 86.15% accuracy.
Keywords
Crowdfunding; Kickstarter; Machine Learning; Deep Learning; Success Prediction;
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Times Cited By KSCI : 1  (Citation Analysis)
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