###### Abstract
In the literature, there are many purposed approaches tackle the problem of spam reviews detection recently, such as supervised learning [18; 19; 20; 21; 22], integrated with sentiment analysis method [23; 24; 25; 26; 27], graph based detection [28; 29], ensemble [30], semi-supervised learning [31; 32; 33], deep learning [34; 35] and others methods like [36; 37; 38]. However, outlining and analyzing all these works and more can be bothersome for others. Therefore, several survey papers were proposed in order to examine, investigate and address different works regarding spam reviews detection. For example, Ref. [15] was one of the first surveys that examined the spam reviews detection works. The survey systematically analyzed and categorized various models of spam reviews detection as well as evaluated them in terms of accuracy and other measures. In the same year, the authors of [39] also introduced a survey that handles the problem of spam reviews detection. Nevertheless, they only considered works that related to machine learning techniques. They stated that most approaches, at that time, focused on supervised learning methods. Furthermore, [PERSON] et al. proposed a study that exchange views of different frameworks adapted for detecting spam reviews [40]. Their paper provided a comprehensive comparison of accuracy levels that tried to solve spam reviews detection. Whereas, [PERSON] et al. presented a survey focused on various machine learning techniques and compare their accuracies for the purpose of identifying spam reviews [41]. Their conclusion suggested that the semi-supervised techniques combined with multi-aspect features have the best performance in detecting spam reviews. As for [8], they summarized and outlined various spam reviews datasets alongside their implemented methods. Also, they categorized the methods into two parts, the neural network model and traditional statistical methods. While [42] conducted a comprehensive review of the spam reviews detection works through utilizing the Systematic Literature Review (SLR) mechanism. More than 70 papers have been reviewed and examined, and then they study the feature extraction techniques in these works. Besides, additional analysis has been preform regarding the metrics, datasets, and performance of the detection approaches. The authors of [43], presented a literature review of the works of spam reviews detection from 2015 to 2020. They identify all studies during that time and scrutinize the findings, research gaps, and similarities of the approaches. Additionally, a three par tition was applied to the analyzed works based on who write spam review, detection methods, and spammer groups. Likewise, [PERSON] et al. proposed a survey according to the antecedent-consequence-intervention technique to investigate spam reviews [44]. Nearly 20 and 18 research questions and propositions, respectively, were determined after examining the literature. Further, due to the lack of excellent datasets, the works on spam reviews did not expand as expected, therefore, a comprehensive outline of the existing public datasets has been presented. On the other hand, [PERSON]. [45] investigated the ground truth of spam reviews detection studies. This review focused on state-of-the-art truth for two classical aspects, expert spammers and crowdsourcing. After their analysis, four conclusions have been reported. Firstly, data collection was more challenging if the spammers are professional. Secondly, the behavioral type was more reliable than the linguistic type. As for the third conclusion, the abnormal activities can be dependable at the same level as spam intentions. While in the fourth point, several reliable facts have been identified, namely, spam cost, deviation, opinion proportion, grouped spamming, review distribution, and grouped spamming. [PERSON] et al. proposed a different survey that focused on spam reviews detection combined with the sentiment analysis method [46].