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Open Access Full size table The average review length may be an important indication of reviewers with questionable intentions since about 80 % of spammers have no reviews longer than 135 words while more than 92 % of reliable reviewers have an average review length of greater than 200 words. Supervised learning can be used to detect review spam by looking at it as the classification problem of separating reviews into two classes: spam and non-spam reviews. To the best of our knowledge, the first researchers to have studied deceptive opinion spam using supervised learning were Jindal et al. [21]. They discuss the evolution of opinion mining, which had primarily focused on extracting or summarizing the opinions from text by using Natural Language Processing (NLP). Prior to their contribution, the content characteristics of the text that might indicate abnormal activities, such as creating review spam, had not been addressed. In an effort to investigate opinion spam in reviews and devise techniques for review spam detection, Jindal et al. collected 5.8 million reviews of products on Amazon generated by 2.14 million users. The authors categorized the reviews of class spam into three types: untruthful opinion, reviews on brand only, and non-review (labeled types 1, 2 and 3 respectively). They started by finding the near duplicate reviews, which they defined as reviews with a Jaccard similarity score of over 90 % of their 2-g. This was done using a method known as w-shinglingFootnote 9. An alternate method for detecting near duplicates using Symantec Language Models (SLM) was developed by Raymond et al. [1]. They then extracted 36 additional features that describe the review, reviewer and product reviewed. A logistic regression model was built using these examples, and when tested using 10-fold cross validation, an Area Under the receiver operating characteristic Curve (AUC) score of 0.78 was achieved when using all features, compared to an AUC score of 0.63 when only using text features. However, the authors recognized that simply finding duplicate reviews is a trivial task and they wanted to test if a model trained using duplicate reviews would generalize to find review spam in general. They manually analyzed 100 non-duplicate reviews that the classifier predicted were spam with the most confidence and found that 52 % were clearly spam. It was also hypothesized that outlier reviews may in fact also be opinion spam and a series of lift curves was constructed to demonstrate the classifier's effectiveness in identifying these outliers as potential spam. Reviewer centric review spam detection Jindal N, Liu B, Lim EP (2010) Finding unusual review patterns using unexpected rules. In: Proceedings of the 19th ACM international conference on Information and knowledge management. (pp. 1549–1552). ACM, Toronto, ON, Canada Google Scholar Dietterich TG (2000) Ensemble methods in machine learning. In: Multiple classifier systems. Springer, Berlin Heidelberg, pp 1–15 camera bag cyber monday
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