## 2 Background, Definitions and Motivation
### Detection  
Detection can be described as a process to prevent and identify something that presents and further perform its purpose either being positive or negative [85; 86; 87; 88; 89; 90]. [PERSON] et al. [91] states that detection is a procedure that required action after the individuals in control become concerned about an event that might cause undesirable and unexpected activity. Whereas, [PERSON] explained the detection as an accumulation of several discrepancies that reached the threshold [92]. Furthermore, detection in the security fields is more critical than detection in other fields. For example, the authors in [93] described security threat detection as a practice of examining the security ecosystem in order to distinguish any malicious activities. The main principle of the detection process relies on the technology perception to discover unique patterns among all behaviors [94]. Besides, the intention of security detection is to implement automatic systems that are capable of investigating the possibility of different kinds of threats [95].  
The detection process might help us with an early warning of the possible conditions that can occur. Thus, when we need a countermeasure for an exact incident, there will be time to construct a plan and execute the steps of that plan. For example, when a person involves in a routine activity like driving, he needs to observe the disturbances that could imply traffic jams or dangerous situations. Even in a stable circumstance (a tree branch could fall on a car or a maintenance procedure that might impact the safety of a plant operation). An alert of possible risk is desired in order to make the correct action to prevent the loss of important resources. As soon as the problem is detected, various procedures can be applied, including, collecting more information, monitoring the events closely, defining the problem, or discussing the situation with others.  
Detection can be found in all fields, especially in security, such as malware, anomaly, intrusion, and spam detection. Malware detection, for instance, refers to the procedure of finding malicious software (e.g., viruses, spyware, and ransomware) on a system and trying to perform extensive damage or steal information of that system [96]. While intrusion detection is consists of a software application that attempt to monitor the system or network for any policy violations malicious activity [97]. In addition to that, anomaly detection is a process of recognizing the outlier or difference of normal activity, usually in a network. As for spam detection, it has more variants than the other detection methods. Spam can be in emails, social network profiles and posts, messages, and, reviews.  
Spam reviews are characterized to be complicated due to their structure. Therefore, complex problems required complex solutions. In literature, many detection methods for spam reviews have been presented. [81] introduced a network-based spam detection model for online reviews in the social network environment, while [98] study the detection of spam reviews using the reviews processing and the rating of that reviews. Deep learning also has been used to identify the spam reviews through [99], and the authors of [100] investigated the spam detection for reviews by integrating the probabilistic review graph with the multi-modal embedded representation. Such methods are two years old and the style of spam reviews has been improved. Consequently, new and enhanced approaches become imperative. The discussion and analysis of the novel methods can be found in Section 3.