Combating Product Review Spam Campaigns via Multiple Heterogeneous Pairwise Features
Abstract
Spam campaigns spotted in popular product review websites (e.g., amazon.com) have attracted mounting attention from both industry and academia, where a group of online posters are hired to collaboratively craft deceptive reviews for some target products. The goal is to manipulate perceived reputations of the targets for their best interests. Many efforts have been made to detect such colluders by extracting pointwise features from individual reviewers/reviewer-groups, however, pairwise features which can potentially capture the underlying correlations among colluders are either ignored or just explored insufficiently in the literature. We observed that pairwise features can be more robust to model the relationships among colluders since they, as the ingredients of spam campaigns, are correlated in nature. In this paper, we explore multiple heterogeneous pairwise features in virtue of some collusion signals found in reviewers' rating behaviors and linguistic patterns. In addition, an unsupervised and intuitive colluder detecting framework has been proposed which can benefit from these pairwise features. Extensive experiments on real dataset show the effectiveness of our method and satisfactory superiority over several competitors.
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Identifying organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions.
“Identifying organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions, e.g., that leverage lockstep behaviors [32, 43, 51, 72, 73, 78, 85, 86, 89, 92, 93] or review bursts [27, 28, 35, 39, 40, 42, 44, 45, 50-53, 53, 5759, 65, 66, 84, 87, 88, 92].”
From RacketStore (2021) · cites this paper as [89]
The study used GDPR-recommended pseudonymisation and deleted all device-to-identity links after data collection.
“We used GDPR [70] recommended pseudonymisation for data processing and statistics, and other generally accepted good practices for privacy preservation.After data collection, we have deleted all device-to-identity links and only generated statistics that allowed us to validate our assumptions.”
From The Art and Craft of Fraudulent App Promotion in Google Play (2019) · cites this paper as [70]
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- Authors
- Xu, C., & Zhang, J.
- Affiliations
- Nanyang Technological University (SG)
- ORCID
- Venue
- Proceedings of the 2015 SIAM International Conference on Data Mining
- Published
- 2015-06-30
- Pages
- 172-180
- OpenAlex
- W2401089081
- Cited by
- 51
- References
- 18
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 9 · 2020: 3 · 2019: 7 · 2018: 10 · 2017: 6 · 2016: 1 · 2015: 2
- FWCI
- 5.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- SIAM International Conference on Data Mining (publishedVersion)
- Funders
- Ministry of Earth Sciences
- Ministry of Education, India
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- heterogeneous features, deceptive reviews, linguistic patterns, unsupervised detection
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