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Nowadays, the development of internet innovation makes a big difference in the way of human's life styles. Variant e-commerce websites, e.g., Yelp, Amazon and Taobao,Footnote 1 provide internet user with a convenient, efficient and relatively reliable online trading environment. More and more merchants prefer to build their virtual shops through different online platforms. Meanwhile, an increasing number of consumers gradually get used to this way of shopping, and automatically share their shop experiences and reviews by using an online review system which applied by the e-commerce website. Because most of these reviews come from the online consumers, they basically reflect the quality of product or the user experience. More and more people are accustomed to checking online reviews before placing an order to buy goods. Moreover, many merchants realize that the more positive online reviews they have, the more business transaction they have and they also can quickly expand and get high reputation. Further, making spam online reviews has become an industrial chain, malicious merchants can easily find some professional spam review writing services online, like the Sponsored Reviews,Footnote 3 which is a site where advertisers and bloggers get in touch to write paid reviews. This deteriorating online review environment let us have to face the task of spam review detection. In this paper, we integrate the untruthful opinions and the fraud one comes from the other two types of reviews, and uniformly called as "spam reviews". Spam audits are conflicting with the genuine assessment of items and attempt to deceive per users or intentionally overestimate or belittle one category things. The source of spam reviews might come from malicious merchants, individual spammers and fraud groups. Spam reviews take the form of various patterns designed by spammers [5, 6]. For occasion, the taking after has appeared two spam surveys was writing to Amazon review platform, which was identified with a model survey spam discovery framework [7]. After observing from "Review 1", it is troublesome for human per users to decide whether the audit is spam or generous. Fortunately, on the off chance that a per user finds these two reviews at the same time, he/she will be able to capture the fundamental spam include to classify these two as spam reviews, due to both of them have settled semantic design almost diverse items. Clearly, the manual approaches of identifying spam audits are not attainable for this event. Review 1: I did broad investigate some time recently selecting the SD60D, and I am excited with my buy. This camera is modest (littler than my iPod) and lightweight, but still takes extraordinary picture. The screen is much bigger than my friends'cameras, and it has all the additional settings that the normal individual should take incredible photographs is all sorts of conditions, I have not had any terrible or hazy pictures with it however. I am excited with this camera and would suggest it to everybody. Review 2: I did broad investigate some time recently selecting the Kodak EasyShare C875, and I am excited with my buy. This camera takes extraordinary picture. The screen is much bigger than my friends'cameras, and it has all the additional settings that the normal individual has to take extraordinary photographs is all sorts of conditions, I have not had any terrible or hazy pictures with it however. I am excited with this camera and would suggest it to everybody. How to utilize the relationship between products, consumers and reviews? This research direction has attracted a lot of research attentions [4, 11,12,13]. Among them, they are mainly focused on basic language models which do not consider deep and relational information. Deep learning models have broadly been connected into numerous NLP assignments. Compared with conventional measurable models, new methods (e.g., deep neural networks and graph based methods) create a large space for new researches [14,15,16]. Recently, a few survey works have been published, there are three works to summarize the existing method for the spam review detection [5, 17, 18]. However, these three works have several shortages. First, they do not systematic summarize the labelled datasets and verify the availability of their listed data source. Secondly, they lack of the conclusion of graph-based technique, especially the rapidly graph convolution network method developed in recent years. Third, they fail to give a complete task classification to cover existing methods. To address these issues, we focus on three aspects to systematically summarize previous research works: existing method and available datasets, and provide some suggestions for future research. Especially, we will disentangle the graph-based strategies that have been proposed to unravel the issue of spam review discovery. Our work first defines the mainly task of spam review detection. Then we present the existing state-of-art approaches, including four types of directions, such as feature engineering, traditional statistical models, neural network models and graph networks frameworks. In addition, we summarize some existing data resources and their data structure. Finally, we provide some construction research direction for the future. camera bag cyber mondaydesigner bag black friday sale

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