基于双循环图的虚假评论检测算法

Double Cycle Graph Based Fraud Review Detection Algorithm

由于对商店的在线评论能给顾客提供许多有价值的信息并极大地影响商店的信誉度,因此,在利益的驱使下出现了大量虚假评论,扰乱了市场秩序。许多商店或个人通过虚假评论故意吹捧或诋毁特定商店,从而达到获利的目的,因此提出有效的虚假评论检测方法至关重要。文中基于大量用户、评论和商店之间的关系构建图过滤器,经过迭代计算获得用户、评论和商店的置信度,从而发现虚假评论。其中包括3个关键问题:获取可靠的用户、评论和商店置信度,有效地辨识真实评论,准确发现虚假评论及虚假用户。针对提高用户、评论和商店置信度的可靠性问题,文中提出了一种循环迭代的方法来获取可靠的用户、评论和商店置信度;为了更加有效地发现虚假评论和虚假用户,设计了一种加权图过滤器,通过与获取的可靠置信度结合,得到了一种双循环图过滤检测算法。将所提检测算法应用到Yelp数据集上展开实验,验证了所虚假检测算法可以有效检测虚假评论。

Because online reviews of stores can provide customers with a lot of valuable information and greatly affect the credibility of stores,a large number of spam reviews are emerged to disturb the order of market for pro- fit .Many stores or individuals deliberately flatter or denigrate certain stores through fake reviews to achieve their profit objectives.Thus an efficient fraud review detection algorithm is crucial.This paper built a graph filter based on the relationships among users,comments and stores, and obtained the reliability of users,comments and stores through iterative calculation,so as to find fake reviews.Three key questions are brought up:to get more reliable reliability of users,comments and stores ,to identify the real reviews effectively,and to detect fake reviews and spammers effectively.In order to improve the reliability of users, comments and stores,a double cycle graph based detection algorithm was proposed to obtain reliable users,comments and stores.In order to find fake reviews and spammers effectively,this paper designed a novel weighted graph filter,through the combination of reliability and obtain reliable,and put forward double cycle filtering detection algorithm.The proposed detection algorithm is applied to Yelp datasets for experiments and proved efficiently in detection of spammers and identifies real reviews.