## 3 Approaches  
Detection methods have emerged to identify and control the situation of spam reviews in industrial and academic institutions. One example of these methods is behavior detection based. This type of detection depends on users' behavior to differentiate them from spammers in order to detect their reviews. The work in [126], for instance, investigates this problem using Generative Adversarial Network (GAN). Where it generates synthetic features behavior of the new users. The work begins by choosing six real behavior features from normal users and training the GAN to generate synthetic features such as rating, attribute, and text features. Moreover, the authors implement a novel generator and discriminator for better training. The process helps the GAN to employ new users in order to generate the synthetic behavior features. The experiments were done on Yelp datasets and the results show that their framework outperforms the other methods. Another example of the behavior detection type is demonstrated by [127]. The paper presented a model to distinguish spam reviews from real ones. This model follows the procedure of an end-to-end training method to exploit the properties of random forest and Autoencoder. The implementation of the decision tree model is applied in order to identify the global parameter of the learning operation. Compared with other methods on the Amazon review dataset, this paper's experiments reveal that the proposed model achieves better results.  
Furthermore, another detection method has also been presented in the literature, namely supervised learning-based. In this type, the labeled instances are used to learn using the machine learning technique. Several supervised learning-based approaches have been applied, one of them was the work in [128]. The authors investigated the detection of fake online reviews that can cause a severe impact on user decisions using supervised classification models. The contribution of this study is begun by extracting different features and then re-engineering them -using Cumulative Relative Frequency Distribution- to improve the detection phase. The evaluation process shows that their approach exceeds other methods on the Yelp dataset. On the other hand, [PERSON]. [129] tried to resolve the falsification of online reviews by using supervised classifiers techniques. This attempt to handle such a problem employed neural approaches to classify the reviews by training the models on syntactic and lexical patterns. The work proceeds by comparing various types of supervised classification models. They applied Google's latter architecture (BERT) in order to detect fake reviews. The approach achieves 90% in terms of accuracy. Likewise, [PERSON]. [130] introduced a detection method (HOTFRED) to identify the online fake reviews in the Tourism Domain. Hoteliers and guests suffer from these kinds of reviews when trying to plan or select the optimal hotels for their journey. HOTFRED considers a dynamic hotel detection system for fake reviews using different analytical approaches. In spite of its excellent ability to detect fake reviews, the system can also serve as an automatic tool for hoteliers to ensure the hotel they choose is the right one for their requirements.  
These aforementioned approaches for both behavior and supervised detection based used widely for detecting spam reviews in literature. Further, many other approaches have been also applied for reviews detection by different researchers, which are addressed in detail in the following subsections. These approaches are more specific and described based on their category, namely Semi-supervised, Deep Learning, Linguistics, Ensemble, Graph-based, Sentiment, and Other approaches.