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• is there a constant concern about potential Joseph D. Prusa1, Review spam can also negatively impact businesses due to loss in consumer trust. The issue is severe enough to have attracted the attention of mainstream media and governments. For example, the BBC and New York Times have reported that "fake reviews are becoming a common problem on the Web, and a photography company was recently subjected to hundreds of defamatory consumer reviews" [1]. In 2014, the Canadian Government issued a warning "encouraging consumers to be wary of fake online endorsements that give the impression that they have been made by ordinary consumers" and estimated that a third of all online reviews were fakeFootnote 1. As review spam is a pervasive and damaging problem, developing methods to help businesses and consumers distinguish truthful reviews from fake ones is an important, but challenging problem. Full size table Ott et al. [12] conducted a more recent study of deceptive opinion spam using the same data and framework as they used earlier [3]; however, they limited their scope to n-gram based features and only used the SVM classifier since it outperformed Naïve Bayes in their earlier work. Using unigram and bigram term frequency features achieved an accuracy of approximately 86 % when considering only reviews with negative sentiment. Again they had human judges evaluate the reviews and found the classifier outperformed them, with the best judge achieving an accuracy of 65 %. They also tested classifier performance when using both the positive and negative reviews together for training and observed that the accuracy on reviews with positive sentiment dropped from 89.3 to 88.4 %. The most notable observation is that doubling the size of the training data, by adding negative sentiment training instances, did not improve results and, in fact, slightly lowered the accuracy of detecting positive sentiment spam reviews. This suggests that separating spam review detection into positive sentiment spam review detection and negative sentiment spam review detection is beneficial. Again, some of the data being used is not real-world data and it remains unknown if the performance of classifiers trained using their data will carry over to purely real world datasets. Additionally, it should be noted that their experiment relied entirely on n-gram features, which were shown by Jindal et al. [21] to be inferior to n-grams in combination with other types of features. Based on our survey, most of the previous studies have focused on supervised learning techniques. However, in order to use supervised learning, one must have a labeled dataset, which can be difficult (if not impossible) to acquire in the area of review spam. From the literature we discussed, it can be observed that most of the available datasets used in the previous studies are synthetically created, most likely due to the lack of review spam examples and the difficulty of labeling them [19]. Building and evaluating classifiers based on these synthetic datasets can be problematic, as it has been observed that they are not necessarily representative of real world review spam. For example, when using the same framework to evaluate the artificial AMT dataset used in [3, 12, 25] and Yelp's filtered reviews dataset, the extracted features and results differed greatly, especially when using n-gram text features [23]. Comparing classification performance across these datasets shows that when evaluated on the synthetic review dataset, the classifier achieved an accuracy of 87 %, but while using Yelp's reviews only achieved 65 % accuracy. This 22 % drop in accuracy implies that synthetically created reviews have different distinguishing features than real-life fake reviews, and that the reviews produced by AMT do not accurately reflect real world spam reviews. Dixit S, Agrawal AJ (2013) Survey on review spam detection. Int J Comput Commun Technol ISSN (PRINT) 4:0975–7449 Google Scholar Classification replica bags?

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