通过检测不可靠的评级评论来隔离欺骗Yelp用户
Quarantine Deceiving Yelp's Users by Detecting Unreliable Rating Reviews
摘要：
在线评论已经成为一个有价值的和重要的资源，不仅对消费者，但公司，在决策。在缺乏可信系统的情况下，高度受欢迎和值得信赖的互联网用户将被假定为可信圈的成员。在本文中，我们描述了我们的重点是识别欺骗Yelp的用户，采用审查尖峰检测（RSD）算法和垃圾邮件检测技术在桥接审查网络（BRN），提取的关键特征。我们发现，Yelp超过80%的账户是不可靠的，超过80%的高评级企业受到垃圾邮件的影响。
英文摘要：
Online reviews have become a valuable and significant resource, for not only consumers but companies, in decision making. In the absence of a trusted system, highly popular and trustworthy internet users will be assumed as members of the trusted circle. In this paper, we describe our focus on quarantining deceiving Yelp's users that employ both review spike detection (RSD) algorithm and spam detection technique in bridging review networks (BRN), on extracted key features. We found that more than 80% of Yelp's accounts are unreliable, and more than 80% of highly-rated businesses are subject to spamming.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
