Content-based Success Prediction of Crowdfunding Campaigns: A Deep Learning Approach.

Despite the huge success of crowdfunding platforms, the average project success rate is 41%, and it has been decreasing. Hence, finding out the factors that lead to successful fundraising and predicting the probability of success for a project has been one of the most important challenges in the crowdfunding. This work is the first attempt to use in-band project content - text - data only, contained in all the Campaign, Updates, and Comments sections of a crowdfunding project (not in combination with any other out-of-band project metadata or statistically-derived numeric features), for success prediction. By adopting (i) the sequence to sequence (seq2seq) deep neural network model with sentence-level attention and (ii) Hierarchical Attention-based Network (HAN) model, we demonstrate that our proposed model achieves the state-of-the-art performance in predicting success of campaigns, as much as 89-91%. We also show that our method achieves 76% accuracy on average on the very first day of project launch, using campaign main text data only.