## 3 Approaches
### Linguistics  
Linguistics or multilingual approaches usually rely on the contextual structure of the text of reviews. The lingual detection systems prove that the issue also occurs in other regions and languages. Furthermore, this kind of system depends more on the language characteristics itself, so most features are text-based (linguistic features). [PERSON] et al. [143] declared that most of the spam review detection studies are more towards languages such as Chinese, Arabic, and English. Hence, in their work, they aim to develop a spam review detection approach based on the Roman Urdu language. The approach is implemented on several classification models based on two types of features, including behavioral features and linguistic features. Three different perspectives were taken into account when the performance was evaluated. First, the linguistic featuresonly were utilized in the classification models. Second, the behavioral features merge with non-distributional and distributional facets for evaluation matter. While the third perspective, both linguistic and behavioral features are combined together and employed for evaluation. The experimental evaluations showed that the best approach was the one with both features combined (linguistic and behavioral) with 0.96 in terms of accuracy. Further, another crucial objective is considered. Increases the trust of the reviews and mitigates the spam reviews from spreading in the South Asian region. Additional recent work also applied the spam review detection method based on the behavioral and linguistic features [144]. Similar to the previous study, two different methods were used to detect spam reviews. First, by utilizing the Behavioral Method (SRD-BM), while the second by utilizing the Linguistic Method (SRD-LM). The SRD-BM used 13 different features, whereas RD-LM considered the textual features for spam reviews detection. The results demonstrated the performance of both methods compared to other state-of-art approaches with 93.1% and 88.5% for SRD-BM and SRD-LM, respectively. The authors of [145] investigated various supervised machine learning to detect spam reviews based on linguistic content-based and Word Count (LIWC). In order to reduce the huge dimensional of the data. Principal Component Analysis (PCA) technique is used. Five different variances with and without PCA variances were employed for the evaluation process as well as several machine learning techniques were applied. The Ensemble Bagged classifier outperforms the other supervised methods with 88% in terms of accuracy. Moreover, the work in [146] presented an unsupervised approach for spam reviews detection on videos, images, and Chinese texts. Their approach outcome various findings after the evaluation process: 1. The amount of image spam is more than the video and text spam; 2. Stealing from reviews is more appealing than just borrowing something from the marketing; 3. Spammers use fictitious rare incidents more than any tricks type in order to influence customers; 4. Utilizing the same methods for texts images and videos is common. [PERSON] and [PERSON] [147] reported that the way how customers understand spam reviews is still not investigated enough. Hence, they designed a theoretical approach that describes the linguistic style of the reviewer's intentions. This approach was evaluated on 120 reviews that were examined by applying the fractional logit model. The conclusions of the results demonstrated that the method used by the speaker shows his intention. Also, reviews with lower argument structuring, flattering and contextual embedding are comprehended by customers as spam reviews more than other reviews. Furthermore, [PERSON]. [148] presented two study for spam reviews detection. In the first study, they used the Yelp dataset that has Linguistic Inquiry Word Count (LIWC) features. While the second study, the reviews were evaluated using 660 participants to label reviews as confident or doubtful.