SC-Com: Spotting Collusive Community in Opinion Spam Detection

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.

Automated summary

The paper proposes an optimized framework for detecting collusive communities, effectively identifying opinion spam reviewers. By detecting collusiveness from behavior and extracting abnormal features for analysis and judgment, the method can effectively and accurately identify spammers.