Secure and smart autonomous multi-robot systems for opinion spammer detection

Reviews of social media have become very important reference indicators for people shopping. However, in order to attract more consumers, some bad merchants organize many fraudulent reviewers to conduct malicious reviews aim at misleading consumers, which is called opinion spammer group. Previous studies have shown that there is an implicit community between reviewers In this paper, we try to use community discovery approach in secure and smart autonomous multi-robot systems for opinion spammer detection, and improve the prediction precision. We propose a novel approach, named SPClique, which is based on the Clique Percolation Method (CPM) to detect opinion spammer groups by modeling the review dataset as reviewer projection graph. During the process, we introduce approximate computing to get a valuable smaller reviewer-projection graph and expand the computing power. It first executes the CPM algorithm in the reviewer-projection graph to find all k-clique clusters, and then innovatively describes an opinion spammer group based on k-clique clusters. Second, the group-based spam and individual-based spams indicators are used to measure the suspiciousness of each opinion spammer group. Finally, it outputs the suspect ranking of opinion spammer groups. The prediction precision of our proposed method is better than four advanced comparison methods, and our approach can detect more real opinion spammers in large-scale review datasets. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a novel method SPClique to detect opinion spammer groups, which shows better prediction precision in large-scale review datasets.