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I call BS: Fraud Detection in Crowdfunding Campaigns
arXiv - CS - Computers and Society Pub Date : 2020-06-30 , DOI: arxiv-2006.16849
Beatrice Perez, Sara R. Machado, Jerone T. A. Andrews, Nicolas Kourtellis

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 performed (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.

中文翻译:

我称之为 BS:众筹活动中的欺诈检测

过去几年,对基于慈善的众筹环境的捐赠一直在增加。不出所料,此类平台中的欺骗和欺诈行为也有所增加,但尚未深入研究以了解哪些特征可以暴露此类行为并允许其自动检测和阻止。事实上,众筹平台是唯一通常对每项服务中发起的活动进行监督的平台。然而,他们没有得到适当的激励来打击用户之间的欺诈以及他们发起的活动:一方面,平台的收入与执行的交易数量成正比(因为平台对每次捐赠收取固定金额);另一方面,如果一个平台在欺诈程度方面是透明的,它可能会阻止潜在捐助者参与。在本文中,我们迈出了研究众筹活动中欺诈行为的第一步。我们分析从不同众筹平台收集的数据,并将 700 个活动注释为欺诈与否。我们计算各种基于文本和图像的特征,并研究它们的分布以及它们如何与竞选欺诈相关联。使用这些属性,我们构建了机器学习分类器,并表明可以自动对此类欺诈行为进行分类,准确率高达 90.14% 和 AUC 高达 96.01%,仅使用发布时活动描述中可用的特征(即,没有用户或金钱活动),使我们的方法适用于用户浏览器上的实时操作。并将 700 个活动注释为欺诈与否。我们计算各种基于文本和图像的特征,并研究它们的分布以及它们如何与竞选欺诈相关联。使用这些属性,我们构建了机器学习分类器,并表明可以自动对此类欺诈行为进行分类,准确率高达 90.14% 和 AUC 高达 96.01%,仅使用发布时活动描述中可用的特征(即,没有用户或金钱活动),使我们的方法适用于用户浏览器上的实时操作。并将 700 个活动注释为欺诈与否。我们计算各种基于文本和图像的特征,并研究它们的分布以及它们如何与竞选欺诈相关联。使用这些属性,我们构建了机器学习分类器,并表明可以自动对此类欺诈行为进行分类,准确率高达 90.14% 和 AUC 高达 96.01%,仅使用发布时活动描述中可用的特征(即,没有用户或金钱活动),使我们的方法适用于用户浏览器上的实时操作。
更新日期:2020-07-01
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