Leveraging deep learning with audio analytics to predict the success of crowdfunding projects

In the social Web era, crowdfunding has become an increasingly important channel for entrepreneurs to raise funds from the crowd for their start-up projects. Previous studies have examined various factors, such as textual information of projects and social capital of investors. However, multimedia information on projects such as audio information was rarely studied for analysing crowdfunding successes. This paper designs a novel audio analytics-based deep learning framework that can extract audio features to predict the fundraising outcomes of these projects. In the proposed framework, we suggest transfer learning to train our models, and multi-task learning to extract the deep features of audios. With the proposed features, our model achieves an 8.28% improvement in F1 and a 7.35% AUC comparing to baselines.