Detecting Fake Online Reviews: An Unsupervised Detection Method With a Novel Performance Evaluation

Fake reviews are critical issues in the online world, as they affect the credibility of e-commerce platforms and undermine consumers' trust. Therefore, fake-review detection is of great significance. Since fake-review detection is an unsupervised problem, most existing methods and performance metrics cannot be applied. In addition, contemporary review manipulations are much more difficult to detect than before. To address these two problems, we first propose a fully unsupervised method with steps of survey research, fake-review feature analysis, fake index estimation, and fake-review selection. Fake-review features can be accurately derived from existing studies and survey research. Second, we propose a recommendation-based performance metric for evaluating fake-review detection methods. This metric differs from traditional binary classification performance metrics, as it can be used on review data with no objective review authenticity classifications. In this research, we utilize Dianping as a case study to evaluate the effectiveness of the proposed detection method and performance metric.