Alazazi, MassaraWang, BinAllan, Tareq2020-01-042020-01-042020-01-07978-0-9981331-3-3http://hdl.handle.net/10125/64048Crowdfunding 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.10 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalData Analytics, Data Mining and Machine Learning for Social Mediadonation-based crowdfundingdonation campaignmachine learningrandom forest.support vector machineConference Paper10.24251/HICSS.2020.306