Success Factors of Donation-Based Crowdfunding Campaigns: A Machine Learning Approach

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dc.contributor.authorAlazazi, Massara
dc.contributor.authorWang, Bin
dc.contributor.authorAllan, Tareq
dc.date.accessioned2020-01-04T07:40:11Z
dc.date.available2020-01-04T07:40:11Z
dc.date.issued2020-01-07
dc.description.abstractCrowdfunding has emerged as an alternative mechanism to traditional financing mechanisms in which individuals solicit financial capital or donation from the crowd. The success factors of crowdfunding are not well-understood, particularly for donation-based crowdfunding platforms. This study identifies key drivers of donation-based crowdfunding campaign success using a machine learning approach. Based on an analysis of crowdfunding campaigns from Gofundme.com, we show that our models were able to predict the average daily amount received at a high level of accuracy using variables available at the beginning of the campaign and the number of days it had been posted. In addition, Facebook and Twitter shares and the number of likes, improved the accuracy of the models. Among the six machine learning algorithms we used, support vector machine (SVM) performs the best in predicting campaign success.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.306
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64048
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectdonation-based crowdfunding
dc.subjectdonation campaign
dc.subjectmachine learning
dc.subjectrandom forest.
dc.subjectsupport vector machine
dc.titleSuccess Factors of Donation-Based Crowdfunding Campaigns: A Machine Learning Approach
dc.typeConference Paper
dc.type.dcmiText

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