Spammers, who manipulate online reviews to promote or suppress products, are flooding in online commerce. To combat this trend, there has been a great deal of research focused on detecting review spammers, most of which design diversified features and thus develop various classifiers. The widespread growth of crowdsourcing platforms has created largescale deceptive review writers who behave more like normal users, that the way they can more easily evade detection by the classifiers that are purely based on fixed characteristics. In this paper, we propose a hybrid semisupervised learning model titled hybrid PU-learning-based spammer detection (hPSD) for spammer detection to leverage both the users' characteristics and the user-product relations. Specifically, the hPSD model can iteratively detect multitype spammers by injecting different positive samples, and allows the construction of classifiers in a semisupervised hybrid learning framework. Comprehensive experiments on movie dataset with shilling injection confirm the superior performance of hPSD over existing baseline methods. The hPSD is then utilized to detect the hidden spammers from real-life Amazon data. A set of spammers and their underlying employers (e.g., book publishers) are successfully discovered and validated. These demonstrate that hPSD meets the real-world application scenarios and can thus effectively detect the potentially deceptive review writers.

中文翻译：

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hPSD：用于产品评论的基于 PU 学习的混合垃圾邮件发送者检测模型


垃圾邮件发送者操纵在线评论来推销或压制产品，他们在在线商务中泛滥成灾。为了对抗这一趋势，已经有大量研究集中在检测垃圾评论发送者上，其中大多数设计了多样化的特征，从而开发了各种分类器。众包平台的广泛增长催生了大规模的欺骗性评论作者，他们的行为更像普通用户，这样他们就可以更容易地逃避纯粹基于固定特征的分类器的检测。在本文中，我们提出了一种混合半监督学习模型，称为基于混合 PU 学习的垃圾邮件发送者检测 (hPSD)，用于垃圾邮件发送者检测，以利用用户特征和用户-产品关系。具体来说，hPSD模型可以通过注入不同的正样本来迭代地检测多种类型的垃圾邮件发送者，并允许在半监督混合学习框架中构建分类器。通过先令注入对电影数据集进行的综合实验证实了 hPSD 优于现有基线方法的性能。然后，hPSD 用于从现实的亚马逊数据中检测隐藏的垃圾邮件发送者。成功发现并验证了一组垃圾邮件发送者及其潜在雇主（例如图书出版商）。这些表明hPSD符合现实世界的应用场景，因此可以有效检测潜在的欺骗性评论作者。