In many cases, our decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers’ opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, we can spot the possibility to detect them from their collusiveness. In this paper, we propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, we extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. We show that our method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, our approach showed prominent performance while only considering primary data such as time and ratings.

在许多情况下，我们的决策过程与在线评论密切相关。但是，越来越多的受雇评论者威胁说垃圾邮件，其目的是通过隐藏真实的消费者意见来误导潜在客户。意见垃圾邮件应集体归档，以伪造真实信息。幸运的是，我们可以发现从它们的共谋中发现它们的可能性。在本文中，我们提出了一种优化的共谋社区检测框架SC-Com。它根据行为的共谋性构建审阅者图，并根据社区之间的相互可疑性将图划分为社区。之后，我们提取基于社区和时间上的异常特征，这对于区分垃圾邮件发送者和其他真实用户至关重要。我们表明，我们的方法可以有效，准确地从他们的集体行为模式中发现串通意见垃圾邮件审阅者。在现实世界的数据集中，我们的方法表现出了出色的性能，同时仅考虑了诸如时间和评级之类的主要数据。