## 3 Approaches
### Other Approaches  
This section addresses various works that have a general theme and don't belong to one of the aforementioned categories. Most of them focus on solving spam reviews using different detection systems, such as Content-Based detection, concept drift benchmark-detection, epistemic belief, and unsupervised learning. For instance, [PERSON]. [165] implemented their detection system through a collective technique to identify spammers and spam reviews (adversarial type). The process began by training two models Content-Based Module (CBM) and Behavior-Based Module (BBM). Afterward, both models were co-trained in order to receive each other feedback. According to their results, their method performs better in determining the adversarial reviews. Another example of this category for detecting spam reviews is proposed by [166]. The work developed a novel automated tool for identifying spam reviews using 1041 respondents. Then a comparison of the non and spam reviews with psycholinguistic deception cues. The findings show that the tool obtained an 81% in terms of accuracy. Moreover, [PERSON]. [167] presented a topic model and reviewer anomaly rate method for spam reviews detection. The authors split the spam reviews into deceptive and content-type. In the first part, the dataset is modeled through an LDA topic that observes the reviews' content type, while in the second part, the abnormality degree index is used to recognize the deceptive reviews. A score was assigned for each review and then merged with adaptive weight calculation according to similarity and abnormal of the reviews. High score reviews consider spam, whilst low score review is recognized as non-spam. [PERSON] et al. [168] investigated the drift problem in spam reviews using two techniques,namely content-based classification, and concept drift benchmark-detection techniques. Four real-world datasets were employed for the evaluation matter, the results show that there is a negative correlation between the performance of spam detection and concept drift. The authors of [169] proposed a study about online reviewers' authenticity detection using algorithmic-based. The study takes into account the epistemic belief role, which is the person's ability to decide between facts and falsehood. More than 300 participants were getting involved in order to classify reviews, spam, and authentic. Using epistemic belief and some kind of justification to reduce the relationships between specificity and exaggeration. While the work in [170] declared that feature selection is one of the important methods to enhance the classification of spam detection and reduce the computation time of the training phase. Hence, they study the impact of various types of feature selection methods in detecting spam reviews. Several feature selection methods have been applied and trained on four classification models. Additionally, an examination performs on three well-known datasets alongside the different types of feature selection methods, including, bigram, unigram, word embedding, and frequency count. Their experimental results prove the effects of using different factors on classification performance. Furthermore, Ref. [171] proposed a detection approach for online reviews manipulation. The authors collected a number of reviews from 500 doctors, consisting of textual feedback and ratings (1 to 5). Their study explored the procedure to verify the negative reviews, then rank the doctors and reduce risks in the healthcare environment. Additional recent research about pseudo-reviews detection presented by [172]. Two studies have been done for the purpose of identifying such reviews. The first study concentrates on ensuring the slight impact on product attitude when it's in an isolation environment. In the second study, both pseudo and authentic reviews combined together, where shows that the impact was negatively on purchase intentions. On the other hand, the work of [173] discussed the aspects of how to increase the confidence in reviews.