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Detecting Fraud in Crowdfunding Projects Based on the Kickstarter Platform Using Machine Learning

Student: Valeriia Matsegora

Supervisor: Ivan Stankevich

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Year of Graduation: 2019

This paper will be devoted to detecting and predicting fraudulent crowdfunding projects in the early stages of the campaign, including the moment before its launch. Recently, more and more papers are devoted to the problem of fraud in the crowdfunding campaigns and the objective of detecting it. We used a sample of 61,806 projects from the Kickstarter platform to build algorithms for predicting fraudulent crowdfunding projects. The sample was obtained using scraping software to collect data from web sources, in particular, from Kickstarter projects web pages. Both basic econometric models, such as logistic regression and LASSO-regression, as well as machine learning methods, such as gradient boosting, were used for the analysis. The main results of this study are the selection of important factors affecting the probability of the fraud in the crowdfunding campaign, such as the tonality of the text and the number of typos in the campaign description text, and the development of algorithms that can predict the probability of fraud in the crowdfunding projects.

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