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Detecting fake-review buyers using network structure: Direct evidence from Amazon
Sherry He1, Brett Hollenbeck1, Gijs Overgoor2
1Anderson School of Management, University of California, Los Angeles, CA 90095.
Summary
Fake reviews undermine online markets, but a new detection method uses product networks to identify manipulation. This approach accurately finds fake review buyers, offering a robust solution against fraudulent online ratings.
Area of Science:
- Computer Science
- Information Science
- E-commerce
Background:
- Online reviews are critical for consumer decisions and e-commerce success.
- Firms are incentivized to use fake reviews, leading to widespread market manipulation.
- Existing fake review detection methods are insufficient, with prevalence increasing.
Purpose of the Study:
- To develop a highly accurate and generalizable method for detecting fake reviews.
- To leverage network analysis for identifying products that purchase fake reviews.
- To create a robust detection approach resistant to text and metadata manipulation.
Main Methods:
- Collected a dataset of Amazon product reviews, including direct observation of sellers buying fake reviews.
- Developed a network-based approach using product reviewer network features.
- Utilized unsupervised clustering methods for fake review buyer detection without ground truth data.
Main Results:
- Products purchasing fake reviews exhibit high clustering in the product reviewer network.
- Network-based features are highly predictive of fake review purchasing.
- Unsupervised clustering accurately identifies fake review buyers by detecting network clusters.
Conclusions:
- Network-based features offer a robust method for detecting fake reviews, outperforming text/metadata analysis.
- The inherent limitations of purchasing reviews make network features difficult to manipulate.
- This approach provides a more reliable solution to combat fake review manipulation in online markets.
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