User-based network embedding for opinion spammer detection

Due to the huge commercial interests behind online reviews, a tremendous amount of spammers manufacture spam reviews for product reputation manipulation. To further enhance the influence of spam reviews, spammers often collaboratively post spam reviews within a short period of time, the activities of whom are called collective opinion spam campaign . The goals and members of the spam campaign activities change frequently, and some spammers also imitate normal purchases to conceal the identity, which makes the spammer detection challenging. In this paper, we propose an unsupervised network embedding-based approach to jointly exploiting different types of relations, e.g., direct common behavior relation, and indirect co-reviewed relation to effectively represent the relevances of users for detecting the collective opinion spammers. The average improvements of our method over the state-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. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Due to the significant commercial interests, there is a proliferation of spam reviews aimed at manipulating product reputation. In order to effectively detect collective opinion spammers, this paper proposes an unsupervised network embedding-based approach that utilizes different types of user relationships to represent relevance. Experimental results show significant improvements in AP and AUC compared to existing solutions.