Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user who carries out the review, and the item being reviewed. As features are extracted for the component representation, a feature-wise review is provided based on behavioral, text-based features and their combination. With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. The lack of labeled datasets is explained and potential solutions are suggested. To help new researchers in the area develop a better understanding, a topic analysis and an overview of future directions is provided in each step of the proposed systematic framework.

翻译：为了提供详尽的文献审查,利用一个框架来审查社会欺诈的发现,该框架考虑到三个关键组成部分:审查本身、进行审查的用户和正在审查的项目。随着为组成部分的表述提取了特征,根据行为、基于文字的特点及其组合提供了专题审查。在这个框架下,对各种办法作了全面的概述,包括监督、半监督和非监督的学习。对欺诈侦查的监督方法进行了介绍并分为两个子类:典型的和深层次的学习。对缺乏标签数据集进行了解释,并提出了潜在的解决办法。为了帮助该地区新的研究人员更好地了解、专题分析和对拟议的系统框架的每一步骤进行未来方向的概述。