虚假评论检测技术综述

随着互联网的发展，用户倾向于在购物、旅游、用餐之前参考线上评论.之后，他们也会发表评论来表达自身意见.线上评论越来越具有价值.评论对用户决策的重要导向作用催生了虚假评论.虚假评论，指用户由于利益、个人偏见等因素发布的不符合产品真实特性的评论.这些虚假评论语言上模仿真实评论，消费者很难识别出来.国内外学者综合运用自然语言处理技术来研究虚假评论检测问题.从特征工程的角度分析，虚假评论检测方法可以分为三类：基于语言特征和行为特征的方法、基于图结构的方法、基于表示学习的方法.主要描述了检测的一般流程，归纳了三类研究方法常用的特征，比较了方法的优缺点，并且介绍了研究常用的数据集.最后探讨了未来研究方向.

Survey on Review Spam Detection Techniques

With the development of the Internet, users tend to refer to online reviews before shopping, travelling, and dining. After that, they write reviews to express their own opinions. Online reviews are increasingly of great value. The significant guiding role of reviews playing in consumers'' decisions has given rise to false comments, which we call review spam. The review spam refers to the comments written by users that do not meet the true characteristics of products, due to factors such as commercial profits and personal bias. Spammers imitate the writing style of true reviewers so that customers can hardly discriminate the review spam. Scholars at home and abroad use natural language processing techniques to detect review spam. From the perspective of feature engineering, review spam detection methods are divided into three types:the linguistic and behavior based, the graph based, and the representation learning based. This survey mainly describes the general process of review spam detection, summarizes feature designing of the models, and makes a comparison among three types of methods. Furthermore, the most commonly used datasets are introduced. Finally, it explores the research directions in the future.