iSRD: Spam review detection with imbalanced data distributions

Internet is playing an essential role for modern information systems. Applications, such as e-commerce websites, are becoming popularly available for people to purchase different types of products online. During such an online shopping process, users often rely on online review reports from previous customers to make the final decision. Because online reviews are playing essential roles for the selling of online products (or services), some vendors (or customers) are providing fake/spam reviews to mislead the customers. Any false reviews of the products may result in unfair market competition and financial loss for the customers or vendors. In this research, we aim to distinguish between spam and non-spam reviews by using supervised classification methods. When training a classifier to identify spam vs. non-spam reviews, a challenging issue is that spam reviews are only a very small portion of the online review reports. This naturally leads to a data imbalance issue for training classifiers for spam review detection, where learning methods without emphasizing on minority samples (i.e., spams) may result in poor performance in detecting spam reviews (although the overall accuracy of the algorithm might be relatively high). In order to tackle the challenge, we employ a bagging based approach to build a number of balanced datasets, through which we can train a set of spam classifiers and use their ensemble to detect review spams. Experiments and comparisons demonstrate that our method, iSRD, outperforms baseline methods for review spam detection.