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

Published: 30 October 2018 Publication History

Abstract

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.

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References

[1]
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).
[2]
Jinwook Chung and Kyumin Lee. 2015. A long-term study of a crowdfunding platform: Predicting project success and fundraising amount. In Proceedings of the 26th ACM Conference on Hypertext & Social Media. ACM, 211--220.
[3]
Ethan Mollick. 2014. The dynamics of crowdfunding: An exploratory study. Journal of business venturing 29, 1 (2014), 1--16.
[4]
Thomas Müllerleile and Dieter William Joenssen. 2015. Key success-determinants of crowdfunded projects: An exploratory analysis. In Data science, learning by latent structures, and knowledge discovery. Springer, 271--281.
[5]
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems. 3104--3112.
[6]
Anbang Xu, Xiao Yang, Huaming Rao, Wai-Tat Fu, Shih-Wen Huang, and Brian P Bailey. 2014. Show me the money!: An analysis of project updates during crowdfunding campaigns. In Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 591--600.
[7]
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1480--1489.

Cited By

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  • (2023)Large-scale Text-to-Image Generation Models for Visual Artists’ Creative WorksProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584078(919-933)Online publication date: 27-Mar-2023
  • (2023)Hidden Indicators of Collective Intelligence in CrowdfundingProceedings of the ACM Web Conference 202310.1145/3543507.3583414(3806-3815)Online publication date: 30-Apr-2023
  • (2023)Experience mining based on text analytics and case-based reasoning to support crowdfunding designElectronic Commerce Research10.1007/s10660-023-09739-9Online publication date: 31-Aug-2023
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  1. Content-based Success Prediction of Crowdfunding Campaigns: A Deep Learning Approach

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    Published In

    cover image ACM Conferences
    CSCW '18 Companion: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing
    October 2018
    518 pages
    ISBN:9781450360180
    DOI:10.1145/3272973
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 30 October 2018

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    Author Tags

    1. crowdfunding
    2. deep learning
    3. kickstarter
    4. natural language processing
    5. success prediction

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    • National Research Foundation of Korea (NRF)

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    CSCW '18 Companion Paper Acceptance Rate 105 of 385 submissions, 27%;
    Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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    Cited By

    View all
    • (2023)Large-scale Text-to-Image Generation Models for Visual Artists’ Creative WorksProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584078(919-933)Online publication date: 27-Mar-2023
    • (2023)Hidden Indicators of Collective Intelligence in CrowdfundingProceedings of the ACM Web Conference 202310.1145/3543507.3583414(3806-3815)Online publication date: 30-Apr-2023
    • (2023)Experience mining based on text analytics and case-based reasoning to support crowdfunding designElectronic Commerce Research10.1007/s10660-023-09739-9Online publication date: 31-Aug-2023
    • (2022)Is Equity Crowdfunding the Leapfrog to Companies’ Success? Financial Performance in ChinaComputational Intelligence and Neuroscience10.1155/2022/78145502022Online publication date: 1-Jan-2022
    • (2022)Hydra: Funding State Prediction for Kickstarter Technology Projects Using a Multimodal Deep LearningInformation Management and Big Data10.1007/978-3-031-04447-2_7(92-107)Online publication date: 20-Apr-2022
    • (2021)A mobile application for assessing the product success on crowdfunding campaign: the development and usability testingJurnal Sistem dan Manajemen Industri10.30656/jsmi.v5i2.40735:2(125-134)Online publication date: 31-Dec-2021
    • (2021)Beyond fans: The relational labor and communication practices of creators on PatreonNew Media & Society10.1177/1461444821102796125:10(2684-2703)Online publication date: 4-Aug-2021
    • (2021)A Multi-platform Study of Crowd Signals Associated with Successful Online FundraisingProceedings of the ACM on Human-Computer Interaction10.1145/34491895:CSCW1(1-19)Online publication date: 22-Apr-2021
    • (2021)Leveraging deep learning with audio analytics to predict the success of crowdfunding projectsThe Journal of Supercomputing10.1007/s11227-020-03595-277:7(7833-7853)Online publication date: 1-Jul-2021
    • (2021)Popularity versus quality: analyzing and predicting the success of highly rated crowdfunded projects on AmazonComputing10.1007/s00607-021-00926-w103:9(1939-1958)Online publication date: 1-Sep-2021
    • Show More Cited By

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