###### Abstract
Moreover, the authors of [47], provided a review of the existing strategies and methods for detecting spam reviews. They performed a taxonomy on the methods related to machine learning techniques. Regarding the most recent surveys, three works have been proposed this year. Starting with an investigation of the spam group detection works addressed by [48]. In this study, the authors suggested -after reviewing a number of papers- that the graph-based detection methods show better performance than the other approaches. Also, the behavioral features obtained the best detection accuracy among other features. Whereas, [PERSON] et al. summarized the available datasets and analyzed the feature extraction techniques of the existing approaches [49]. Also, the work-study and compared the standard machine learning techniques against the deep learning methods performance in detecting spam reviews. Further, current gaps and a future direction of the research domain have been discussed. [PERSON] and [PERSON] [50] presented a novel survey that covers the commercial and basic research solutions. Also, the reasons behind the limitation of current approaches for the detection methods. The previously mentioned surveys covered various aspects of spam reviews detection works, ranging from, performance comparison of specific techniques, feature extraction used, measures, available datasets, analysis and suggestion of best methods, approaches limitation, to the future works of the research area. In order to identify relevant articles for this survey, selection criteria were required. The selection criteria comprise inclusion and exclusion phases. If a duplicate article showed up in the inclusion list more than once from different sources, it will still be included only once. For the selection of related articles, inclusion criteria were used sequentially as follows:  
* Articles whose titles are related to some or all of the search keywords. * Articles that contain keywords that are subsets of the search keywords. * Articles that their abstract portray spam reviews detection (or its synonym). * Articles that suggest new models or techniques for spam reviews detection or alter an existing one. * Articles that used already-existing spam reviews detection methods in their phase of the experiment. While the exclusion criteria for removing irrelevant articles follow the following points:
* Articles published before 2020 or those not published during the COVID-19 pandemic. * Articles that do not have or include experiments on spam reviews detection. * Articles that do not meet any of the inclusion criteria. This up-to-date survey differentiates from the previous surveys in consecrating on the works that were proposed during the COVID-19 situation. And also due to the rapid alteration and increase of spam reviews in this period. In addition, a detailed background of the related concepts has been introduced including, online reviews, spam reviews, detection methods, and datasets. Categorize the detection methods into several groups (see Figure 1) and analyze the mechanism used for each category alongside their limitation, strength and provide solutions to enhance their performance. Also, this survey identifies the differences, development, and outcome of the spam reviews works prior to and during the COVID-19 pandemic, namely:  
* The increase of reviews amounts due to the rise of people staying inside. * The behavior of reviews differentiates between the two periods in products, services, and consumers' target, style of context, and their important impact. * Spam reviews also show growth in numbers thanks to the increase of individuals reading and searching for reviews. * Detection technique models improve and become more varied as can be seen in Section 3. Therefore, the contribution of this work is summarized in the following points:  
* Outline the previous spam detection surveys and their contributions. * Provide a background investigation about the concepts of spam reviews detection environment and specify their definitions and motivations.