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Early reviews, posted on online review sites shortly after products enter the market, are useful for estimating long-term evaluations of those products and making decisions. However, such reviews can be influenced easily by anomalous reviewers, including malicious and fraudulent reviewers, because the number of early reviews is usually small. It is therefore challenging to detect anomalous reviewers from early reviews and estimate long-term evaluations by reducing their influences. We find that two characteristics of heterogeneity on actual review sites such as Amazon.com cause difficulty in detecting anomalous reviewers from early reviews. We propose ideas for consideration of heterogeneity, and a methodology for computing reviewers’ degree of anomaly and estimating longterm evaluations simultaneously. Our experimental evaluations with actual reviews from Amazon.com revealed that our proposed method achieves the best performance in 19 of 20 tests compared to state-of-the-art methodologies. key words: early reviews, heterogeneity, data mining, bipartite graph

Detecting Anomalous Reviewers and Estimating Summaries from Early Reviews Considering Heterogeneity