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Analysing and Predicting Success of Crowdfunding Campaigns
Conference proceeding

Analysing and Predicting Success of Crowdfunding Campaigns

Glenn Jin Wee Chia and Kwan Hui Lim
Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries, pp.1-3
ACM Conferences
JCDL '24: 24th ACM/IEEE Joint Conference on Digital Libraries
16/12/2024

Abstract

Computing methodologies -- Machine learning Human-centered computing -- Collaborative and social computing Information systems -- Information systems applications -- Data mining
Crowdfunding have gained popularity as a platform for individuals to harness the power of social solidarity to raise public funds for a range of objectives, such as for community projects, social good, medical expenses, etc. While there are many crowdfunding campaigns with altruistic goals, not all campaigns go viral and many more do not even meet their fundraising targets. This paper analyses the various factors that contribute to successful campaigns and develops five models for predicting fundraising success based on various combination of campaign features. We experiment on a dataset of 18,473 crowdfunding campaigns and discuss our main findings in terms of how different factors affect campaign success.

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