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Due to the huge commercial interests behind online reviews, atremendousamount of spammers manufacture spam reviews for product reputationmanipulation. To further enhance the influence of spam reviews, spammers oftencollaboratively post spam reviewers within a short period of time, theactivities of whom are called collective opinion spam campaign. As the goalsand members of the spam campaign activities change frequently, and somespammers also imitate normal purchases to conceal identity, which makes thespammer detection challenging. In this paper, we propose an unsupervisednetwork embedding-based approach to jointly exploiting different types ofrelations, e.g., direct common behaviour relation and indirect co-reviewedrelation to effectively represent the relevances of users for detecting thecollective opinion spammers. The average improvements of our method over thestate-of-the-art solutions on dataset AmazonCn and YelpHotel are[14.09%,12.04%] and [16.25%,12.78%] in terms of AP and AUC, respectively.

User-based Network Embedding for Collective Opinion Spammer Detection