Collective Opinion Spam Detection: Bridging Review Networks and Metadata
Abstract
Online reviews capture the testimonials of "real" people and help shape the decisions of other consumers. Due to the financial gains associated with positive reviews, however, opinion spam has become a widespread problem, with often paid spam reviewers writing fake reviews to unjustly promote or demote certain products or businesses. Existing approaches to opinion spam have successfully but separately utilized linguistic clues of deception, behavioral footprints, or relational ties between agents in a review system.
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Fraudulent behaviors, including fake reviews and sockpuppet accounts, are prevalent on popular online peer-opinion sites and negatively affect users.
“Popular online services that provide millions of users with access to products, news, social relationships and peer-opinions, are besieged by fraudulent behaviors, that skew public opinion and bias product reputation and popularity [25,35,45,54,75,80,91].”
From The Art and Craft of Fraudulent App Promotion in Google Play (2019) · cites this paper as [45]
Because multiple fraud workers usually target one subject, binary classification of fraud is insufficient to understand and model fraud.
“The insight that multiple fraud workers usually target a single subject suggests that a binary classification of fraud, e.g., fake vs. honest reviews, fraudulent vs. honest accounts [17,26,27,31,32,44,47,80], is insufficient to understand and model fraud.”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [47]
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- Authors
- Rayana, S., & Akoglu, L.
- Affiliations
- Stony Brook University (US)
- ORCID
- Akoglu, Leman
- Venue
- Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2015-08-10
- Pages
- 985-994
- OpenAlex
- W2064058256
- Cited by
- 604
- References
- 27
- Citations by year
- 2026: 49 · 2025: 79 · 2024: 69 · 2023: 58 · 2022: 56 · 2021: 83 · 2020: 67 · 2019: 52 · 2018: 45 · 2017: 37 · 2016: 9
- FWCI
- 26.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- National Science Foundation (1452425,1017181)
- Army Research Office (W911NF-14-1-0029)
- 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
- opinion spam, online reviews, fake reviews, deception detection, relational ties, collective opinion
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