Reviews of product or stores exist extensively in online e-commerce platform which is important for customers to make decisions. For economic reasons some dishonest people are employed to write fake reviews which is also called “opinion spamming” to promote or demote target products and services. Previous researches have made use of text similarity, linguistics, rating patterns, graph relationship and other behaviors for spammer detection. They mainly utilized product review list while it is difficult to find fake reviews by glancing over product reviews in time-descending order. Meanwhile there exists lots of useful information in the list of reviews of reviewers and relationships between reviewers when reviewers commonly reviewed the same stores. We propose the concept of review group and to the best of our knowledge, it’s the first time the review group concept is proposed and used. Review grouping algorithm is designed to effectively split reviews of reviewer into groups which participate in building novel grouping models to identify both positive and negative deceptive reviews. Several new features which are language independent based on group model are constructed. Additionally, we explore the collusion relationship between reviewers to build reviewer group collusion model. Evaluations show that the review group method and reviewer group collusion models can effectively improve the precision by 4%–7% compared to the baselines in fake reviews classification task especially when reviews are posted by professional review spammers.

在线电子商务平台中广泛存在对产品或商店的评论，这对于客户做出决定很重要。出于经济原因，一些不诚实的人被雇用撰写虚假评论，也被称为“意见垃圾邮件”，以促销或降级目标产品和服务。先前的研究已经利用文本相似性，语言学，评级模式，图形关系和其他行为来进行垃圾邮件发送者检测。他们主要利用产品评论列表，而通过按时间降序浏览产品评论很难找到虚假评论。同时，当审阅者通常审阅同一家商店时，审阅者的评论列表中以及审阅者之间的关系中存在许多有用的信息。我们提出审核小组的概念，并据我们所知，这是第一次提出和使用审核小组的概念。审阅分组算法旨在有效地将审阅者的评论分为多个组，这些组参与构建新颖的分组模型以识别正面和负面的欺骗性评论。构造了几个基于组模型与语言无关的新功能。此外，我们探索了审稿人之间的串通关系，以建立审稿人组串通模型。评估显示，与假评论分类任务中的基准相比，评论组方法和评论者组合谋模型可以有效地将精度提高4％–7％，特别是当评论由专业评论垃圾邮件发送者发布时。审阅分组算法旨在有效地将审阅者的评论分为多个组，这些组参与构建新颖的分组模型以识别正面和负面的欺骗性评论。构造了几个基于组模型与语言无关的新功能。此外，我们探索了审稿人之间的串通关系，以建立审稿人组串通模型。评估显示，与假评论分类任务中的基准相比，评论组方法和评论者组合谋模型可以有效地将精度提高4％–7％，特别是当评论由专业评论垃圾邮件发送者发布时。审阅分组算法旨在有效地将审阅者的评论分为多个组，这些组参与构建新颖的分组模型以识别正面和负面的欺骗性评论。构造了几个基于组模型与语言无关的新功能。此外，我们探索了审稿人之间的串通关系，以建立审稿人组串通模型。评估显示，与假评论分类任务中的基准相比，评论组方法和评论者组合谋模型可以有效地将精度提高4％–7％，特别是当评论由专业评论垃圾邮件发送者发布时。此外，我们探索了审稿人之间的串通关系，以建立审稿人组串通模型。评估显示，与假评论分类任务中的基准相比，评论组方法和评论者组合谋模型可以有效地将精度提高4％–7％，特别是当评论由专业评论垃圾邮件发送者发布时。此外，我们探索了审稿人之间的串通关系，以建立审稿人组串通模型。评估显示，与假评论分类任务中的基准相比，评论组方法和评论者组合谋模型可以有效地将精度提高4％–7％，特别是当评论由专业评论垃圾邮件发送者发布时。