Spotting opinion spammers using behavioral footprints
Abstract
Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or to demote some target products. In recent years, fake review detection has attracted significant attention from both the business and research communities. However, due to the difficulty of human labeling needed for supervised learning and evaluation, the problem remains to be highly challenging. This work proposes a novel angle to the problem by modeling spamicity as latent. An unsupervised model, called Author Spamicity Model (ASM), is proposed. It works in the Bayesian setting, which facilitates modeling spamicity of authors as latent and allows us to exploit various observed behavioral footprints of reviewers. The intuition is that opinion spammers have different behavioral distributions than non-spammers. This creates a distributional divergence between the latent population distributions of two clusters: spammers and non-spammers. Model inference results in learning the population distributions of the two clusters. Several extensions of ASM are also considered leveraging from different priors. Experiments on a real-life Amazon review dataset demonstrate the effectiveness of the proposed models which significantly outperform the state-of-the-art competitors.
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The paper found rapid review bursts in both qualitative and quantitative investigations, consistent with assumptions in prior fraud-detection work, and interview participants revealed adaptive rate control as an avoidance technique.
“We found ASO workers who post fake reviews in rapid bursts in both our qualitative and quantitative investigations.This is consistent with assumptions made in previous fraud detection work, e.g., [26, 28, 32, 36-38, 40, 42, 43, 51, 52, 59-61, 63, 65, 92, 95, 96, 100].”
From The Art and Craft of Fraudulent App Promotion in Google Play (2019) · cites this paper as [36]
These results confirm DDA's outcome and previous work on crowdsourced review manipulation.
“These results confirm DDA's outcome and previous work on crowdsourced review manipulation, e.g., [36].”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [36]
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- Authors
- Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R.
- Affiliations
- University of Illinois Chicago (US)
- Hewlett-Packard (United States) (US)
- HP Labs, Palo Alto, CA, USA
- Venue
- Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2013-08-11
- Pages
- 632-640
- OpenAlex
- W2016266039
- Cited by
- 426
- References
- 47
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 13 · 2023: 21 · 2022: 37 · 2021: 37 · 2020: 61 · 2019: 47 · 2018: 55 · 2017: 47 · 2016: 35 · 2015: 40 · 2014: 17 · 2013: 1 · 2012: 1
- FWCI
- 24.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- National Science Foundation
- 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
- fake review detection, Bayesian clustering, reviewer behavior, Amazon reviews
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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