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

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Abstract

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.

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Notes

  1. Empirically, we found that splitting an individual set for pre-training would help the system’s generalization ability.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 71771212, U1711262).

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  1. School of Information, Renmin University of China, Beijing, 100872, People’s Republic of China

    Jiatong Shi, Kunlin Yang, Wei Xu & Mingming Wang

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  1. Jiatong Shi
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  2. Kunlin Yang
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Correspondence to Mingming Wang.

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Shi, J., Yang, K., Xu, W. et al. Leveraging deep learning with audio analytics to predict the success of crowdfunding projects. J Supercomput 77, 7833–7853 (2021). https://doi.org/10.1007/s11227-020-03595-2

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