| Summary: | With the development of e-commerce, more and more users begin to post reviews or comments about the quality of products on the internet. Meanwhile, people usually read previous reviews before purchasing online products. However, people are frequently deceived by deceptive opinion spam, which is usually used for promoting the products or damaging their reputations because of economic benefit. Deceptive opinion spam can mislead people's purchase behavior, so the techniques of detecting deceptive opinion spam have extensively been researched in past ten years. In particular, some work based on deep learning has been investigated in last three years for the task. However, there still lack a survey, which can systematically analyze and summarize the previous techniques. To address this issue, this paper first introduces the task of deceptive opinion spam detection. Then, we summarize the existing dataset resources and their construction methods. Third, existing methods are analyzed from two aspects: traditional statistical methods and neural network models. Finally, we give some future directions of the task.
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