## 2 Background, Definitions and Motivation
### Datasets  
A dataset in general is a collection of data that take the shape of tabular and can be found in every research domain, for example, security, medical, business, geoscience, etc. These datasets have various forms based on their structure and properties. The structure of the dataset is usually what determines the research problem, namely, supervised, semi-supervised and unsupervised learning. Technically speaking, without datasets machine learning can't perform and execute. Data is considered a significant part of machine learning models that required several procedures to be generated. Such procedures range from collecting, cleaning, pre-processing, formatting to labeling.  
Regarding the spam reviews datasets, there are three types used commonly in the literature, content, meta-data, and product information. Each of which has its own advantage and disadvantage traits [15]. The content-type data, for instance, is consists of the review textual features. That is to say, it is a linguistic feature extracted from the review content. These features can be POS n-grams or words extracted for the purpose of identifying reviews' origin. Despite the fact that this type has a significant role in detecting spam reviews. The methods used are not sufficient to distinguish all kinds of spam reviews.  
Further, the second type (meta-data) is more relevant for the review details besides its actual content, such as the identity of the reviewer, IP, and MAC, and IP addresses, rating, time of the review, geolocation place and review writing time duration. By examining such data, certain malicious behaviors can be identified. For example, when various user-ids post a number of negative and positive reviews of a certain product using the same device it shows suspicious behavior. Also, some reviewers write positive reviews for a particular brand and at the same time, negative reviews for other brands might consider doubtful reviews. Additionally, reviews for a specific hotel were found to be near its location; these reviews obviously not trustworthy because reviewers should be in other places. This type considers effective for spam reviews detection, however, the number of features is limited.  
As for product information datasets, this type takes into account the product sales number and description to detect spam reviews. An example of such type, when a low sales product has too many positive reviews demonstrates the reliability of the reviews. Besides, there are some datasets that combined two or three types together in order to increase the performance of the detection. More experts can distinguish the spam reviews due to an increase in employed features.  
Creating and generating spam review datasets consider very complex. Few researchers adopt the new alternative method for labeling and collecting artificial reviews. By creatingsynthetic spam reviews datasets and taking existing real reviews to builds their model. An example of the context of the spam and real reviews available at [101].  
Several numbers of the proposed spam reviews datasets in the literature are summarized in Table 1. Also, the distribution of the sources of the datasets can be found in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \# & **Source** & **Volume** & **Reference** \\ \hline
1 & Amazon & 5.8 million & [102] \\
2 & Epinions & 6000 & [103] \\
3 & Tripadvisor & 3032 & [104] \\
4 & Yelp & - & [105] \\  
atatang & 9765 & [121] \\
23 & Tripadvisor & 800 & [122] \\
24 & Amazon & - & [123] \\
25 & Amazon & 142.8 million & [124] \\
26 & Yelp & 18,912 & [125] \\
27 & Opinions & 6000 & [103] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of spam reviews datasets in literature.  
Figure 2: Number of datasets for each website.