EAGER: An Open Mobile App Platform to Support Research on Fraudulent Reviews

在在线同行评议网站上取得成功的压力，为搜索排名欺诈创造了一个黑市。诈骗工作人员可能控制着数百个用户账户，他们通过众包网站与产品开发人员联系，然后从他们控制的账户发布虚假活动、评级和对网站所有者产品的评论。大多数同行审查系统使用欺诈检测来过滤虚假活动，但欺诈行为仍然存在。学术欺诈检测研究一直受到以下因素的阻碍：缺乏经过验证的欺诈数据，缺乏评估和比较解决方案的平台，以及商业同行审查网站不愿分享见解、算法和数据。该项目正在开发一个开放的移动应用程序平台，这是第一个协作环境，供研究社区合乎道德地委托和共享经过验证的欺诈数据，对观察到的欺诈发布行为进行分类，评估欺诈检测算法，并试验应用程序市场的内部功能。该平台有可能显著推进欺诈检测研究，并使其与商业同行审查网站更相关，从而帮助减少其数百万用户每天面临的欺诈风险。该项目将调查、开发和评估一个在线框架，以研究应用程序市场中的搜索排名欺诈。该团队正在构建一个开源应用程序市场，以收集地面真相欺诈数据集，验证现有和发现新的欺诈行为，并在现场环境中评估欺诈检测解决方案。该小组将制定与欺诈工作人员互动的协议，评估他们发布的数据的质量，使用对其欺诈性、欺诈行为的归属以及与商业网站上发布的欺诈行为的相似性的保证。该团队将开发新的技术来验证欺诈检测和预防算法的输出，这些算法将参与欺诈的工作人员转变为人类先知。为了帮助弥合欺诈检测解决方案所做的假设和欺诈工作人员采用的策略之间的差距，该团队将开发半结构化问卷，并对从众包网站招聘的欺诈工作人员进行用户研究，以确定并分类他们最受欢迎的欺诈偏好、限制、能力和规避策略。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The pressure to succeed in online, peer-review websites has created a black market for search rank fraud. Fraud workers, who may control hundreds of user accounts, connect with product developers through crowdsourcing sites, then, from the accounts that they control, post fake activities, ratings, and reviews for site-owners' products. Most peer-review systems use fraud detection to filter out fake activities, but fraud nevertheless persists. Academic fraud detection research has been hampered by a scarcity of validated fraud data, the lack of a platform on which to evaluate and compare solutions, and the unwillingness of commercial peer-review sites to share insights, algorithms, and data. This project is developing an open mobile app platform, the first collaborative environment for the research community to ethically commission and share validated fraud data, classify observed fraud posting behaviors, evaluate fraud detection algorithms, and experiment with the inner functionality of an app market. This platform has the potential to significantly advance fraud detection research and make it more relevant to commercial peer-review sites, thus helping reduce the daily exposure to fraud of their millions of users.This project will investigate, develop and evaluate an online framework to study search rank fraud in app markets. The team is building an open-source app market to collect ground truth fraud datasets, validate existing and discover new fraud behaviors, and evaluate fraud detection solutions in a live environment. The team will develop protocols of interaction with fraud workers that will evaluate the quality of the data that they post using assurances of its fraudulence, attribution of fraud, and similarity to fraud posted in commercial sites. The team will develop new techniques to validate the output of fraud detection and prevention algorithms that transform participating fraud workers into human oracles. To help bridge the gap between assumptions made by fraud detection solutions and strategies employed by fraud workers, the team will develop semi-structured questionnaires and conduct user studies with fraud workers recruited from crowdsourcing sites, to identify and classify their most popular fraud preferences, constraints, capabilities and evasion strategies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.