社会欺诈检测综述:方法、挑战与分析(CS)

社交评论主导了网络，并成为产品信息的可信来源。个人和企业利用这些信息进行决策。企业还利用社会信息传播虚假信息，使用单个用户、用户组或经过培训的生成欺诈内容的机器人。许多研究提出了基于用户行为和评论文本的方法来解决欺诈检测的挑战。为了提供详尽的文献回顾，我们使用一个框架来回顾社会欺诈检测，该框架考虑了三个关键组成部分:回顾本身、执行回顾的用户和被回顾的项目。当为组件表示提取特征时，基于行为、基于文本的特征及其组合提供了一个基于特征的回顾。在这个框架下，提出了包括监督学习、半监督学习和非监督学习在内的方法的全面概述。介绍了欺诈检测的监督方法，并将其分为两个子类;古典，和深度学习。解释了缺乏标记数据集的原因，并提出了潜在的解决方案。为了帮助新的研究人员在该领域发展一个更好的理解，主题分析和未来方向的概述提供了提出的系统框架的每一步。

原文题目：Social Fraud Detection Review: Methods, Challenges and Analysis

原文：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.