Library Header Image
LSE Research Online LSE Library Services

I call BS: fraud detection in crowdfunding campaigns

Perez, Beatrice, Machado, Sara ORCID: 0000-0002-9287-8165, Andrews, Jerone and Kourtellis, Nicolas (2022) I call BS: fraud detection in crowdfunding campaigns. In: WebSci 2022 - Proceedings of the 14th ACM Web Science Conference. ACM International Conference Proceeding Series. Association for Computing Machinery, New York, NY, pp. 1-11. ISBN 9781450391917

Full text not available from this repository.

Identification Number: 10.1145/3501247.3531541


Donations to charity-based crowdfunding environments have been on the rise in the last few years. Unsurprisingly, deception and fraud in such platforms have also increased, but have not been thoroughly studied to understand what characteristics can expose such behavior and allow its automatic detection and blocking. Indeed, crowdfunding platforms are the only ones typically performing oversight for the campaigns launched in each service. However, they are not properly incentivized to combat fraud among users and the campaigns they launch: on the one hand, a platform's revenue is directly proportional to the number of transactions (since the platform charges a fixed amount per donation); on the other hand, if a platform is transparent with respect to how much fraud it has, it may discourage potential donors from participating. In this paper, we take the first step in studying fraud in crowdfunding campaigns. We analyze data collected from different crowdfunding platforms, and annotate 700 campaigns as fraud or not. We compute various textual and image-based features and study their distributions and how they associate with campaign fraud. Using these attributes, we build machine learning classifiers, and show that it is possible to automatically classify such fraudulent behavior with up to 90.14% accuracy and 96.01% AUC, only using features available from the campaign's description at the moment of publication (i.e., with no user or money activity), making our method applicable for real-time operation on a user browser.

Item Type: Book Section
Official URL:
Additional Information: © 2022 ACM.
Divisions: LSE Health
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
H Social Sciences > HG Finance
Date Deposited: 20 Jul 2022 15:27
Last Modified: 05 Jul 2024 21:10

Actions (login required)

View Item View Item