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| Content Provider | ACM Digital Library |
|---|---|
| Author | Morales, Alex Sun, Huan Yan, Xifeng |
| Abstract | Online reviews have been popularly adopted in many applications. Since they can either promote or harm the reputation of a product or a service, buying and selling fake reviews becomes a profitable business and a big threat. In this paper, we introduce a very simple, but powerful review spamming technique that could fail the existing feature-based detection algorithms easily. It uses one truthful review as a template, and replaces its sentences with those from other reviews in a repository. Fake reviews generated by this mechanism are extremely hard to detect: Both the state-of-the-art computational approaches and human readers acquire an error rate of 35%-48%, just slightly better than a random guess. While it is challenging to detect such fake reviews, we have made solid progress in suppressing them. A novel defense method that leverages the difference of semantic flows between synthetic and truthful reviews is developed, which is able to reduce the detection error rate to approximately 22%, a significant improvement over the performance of existing approaches. Nevertheless, it is still a challenging research task to further decrease the error rate. Synthetic Review Spamming Demo: www.cs.ucsb.edu/~alex_morales/reviewspam/. |
| Starting Page | 1088 |
| Ending Page | 1096 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781450321747 |
| DOI | 10.1145/2487575.2487688 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-08-11 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Review spam Classification Spam detection |
| Content Type | Text |
| Resource Type | Article |
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