# Spam Review Detection Techniques: A Systematic Literature Review
###### Abstract
Recent media news from the New York Times and BBC [2] have stated that \"nowadays, spam reviews are very frequent on websites, and recently a photography company was exposed to thousands of customer spam reviews\". Hence, detecting spam reviews appears to be a key area, and without solving this important issue, online review sites could become a place full of lies and completely useless [3]. To counter this issue, commercial review hosting sites, such as Yelp (www.yelp.com) and Amazon (www.amazon.com), have already made some progress in detecting spam reviews. However, there is still a lot of room for improvement in spam review detection techniques [4]. Generally, spam review detection approaches consist of the following steps. The first basic step is the gathering of the review dataset; since review datasets mostly consist of unstructured text and may contain noisy data, there is almost always a need to pre-process the datasets. The next step is to select a feature engineering approach, such as linguistic n-gram or an individual spammer-based features approach. Finally, different review spam detection techniques, such as machine learning and Lexicon-based techniques, are applied to figure out which reviews are spam. The focus of this systematic literature review is to analyze, classify, and summarize the context of the productive research in the domain of spam review detection. Until now, there exists only a few survey papers [5; 6; 7; 8; 9] in the domain of opinion mining, and the focus of these studies has only been on Facebook posts, twitter data, discussion group, newsgroup posts, E-mail spam, and spam review detection models in general, whereas the current research also focuses on publicly available review datasets and feature engineering techniques to extract useful features or attributes from the datasets. Furthermore, the researchers propose a taxonomy of spam review detection methods and discusses different parameters that are used to evaluate the performance of spam review detection methods. To the best of the researchers' knowledge, this is the first attempt to compile all the work on spam review detection using the SLR process. In this study, the researchers formalize the basic search string to collect the most relevant studies in the domain of spam review detection. Studies from reputed journals and conferences are analyzed for this SLR. This study has selected 76 primary studies out of 1690 potential primary studies. The preferred studies are evaluated through multiple aspects. This SLR proposes to answer the following research questions (RQs):  
**RQ1:**: Which feature engineering techniques are used for construction or extraction of features from review datasets? **RQ2:**: What methods are used to solve the problem of spam review detection? **RQ3:**: Which performance metrics are used to evaluate the performance of spam review detection methods?The two main contributions of this study are as follows: (i) Provide researchers and practitioners with insight and further improvement prospects on the spam review detection problem. (ii) Present the accuracy of existing spam review detection methods to identify the most effective methods. The answer to RQ1 will help to build an understanding of suitable feature engineering approaches, elaborate commonly used features, and list openly accessible datasets in the domain of spam review detection. RQ2 will identify the existing spam review detection methods, devise taxonomy of approaches, and present a comprehensive comparison of the accuracy of those methods. The answer to RQ3 will help to identify different performance evaluation metrics for the spam review detection methods and present a comparative analysis of these matrices. The remaining paper is divided into following main sections: Section 2 describes the details of the systematic literature review (plan, conduct, and analysis), Section 3 discusses the answers to research questions and addresses the open issues in the area of spam review detection.