Abstract Online reviews play a significant role in influencing decisions made by users in day-to-day life. The presence of reviewers who deliberately post fake reviews for financial or other gains, however, negatively impacts both users and businesses. Unfortunately, automatically detecting such reviewers is a challenging problem since fake reviews do not seem out-of-place next to genuine reviews. In this paper, we present a fully unsupervised approach to detect anomalous behavior in online reviewers. We propose a novel hierarchical approach for this task in which we (1) derive distributions for key features that define reviewer behavior, and (2) combine these distributions into a finite mixture model. Our approach is highly generalizable and it allows us to seamlessly combine both univariate and multivariate distributions into a unified anomaly detection system. Most importantly, it requires no explicit labeling (spam/not spam) of the data. Our newly developed approach outperforms prior state-of-the-art unsupervised anomaly detection approaches.

中文翻译：

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检测异常在线评论者：一种使用混合模型的无监督方法

摘要 在线评论在影响用户在日常生活中做出的决定方面发挥着重要作用。然而，为了经济或其他利益而故意发布虚假评论的评论者的存在会对用户和企业产生负面影响。不幸的是，自动检测此类评论者是一个具有挑战性的问题，因为虚假评论似乎与真实评论不相上下。在本文中，我们提出了一种完全无监督的方法来检测在线评论者的异常行为。我们为此任务提出了一种新的分层方法，其中我们 (1) 导出定义评论者行为的关键特征的分布，以及 (2) 将这些分布组合成一个有限混合模型。我们的方法是高度可推广的，它允许我们将单变量和多变量分布无缝组合成一个统一的异常检测系统。最重要的是，它不需要对数据进行明确的标记（垃圾邮件/非垃圾邮件）。我们新开发的方法优于先前最先进的无监督异常检测方法。