Abstract

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

Highlights

  • Nowadays, websites have become the main source for individuals to express themselves

  • The whole process of spam review detection relies upon the selection of a suitable feature engineering approach because the extracted features from the dataset become the input for the spam review detection method

  • This study presented a systematic literature review of the spam review detection domain and highlighted recent research contributions in the form of different feature engineering approaches, spam review detection methods, and different measures used for performance evaluation

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Summary

Introduction

Websites have become the main source for individuals to express themselves. People can share their views about products and services by using e-commerce sites, forums, and blogs. Most people read reviews about product and services before buying them. Everybody on the web is acknowledging the importance of these online reviews for other customers and for vendors too. Vendors are capable of designing their additional marketing strategies based on these reviews [1].

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