Deep learning for comment spam and scams

Abstract

As the popularity of online social networks has increased, so has the manner in which people purchase online. Online reviews on the purchasing of items or the provision of services have become the primary source of user opinion. When a substantial body of knowledge assesses a product or service, that body's effect on the market is significant. Because of this, concerns have been raised among manufacturers and merchants, who frequently compose rubbish reviews to promote or denigrate the quality of specific items or services in order to make a profit or maintain their reputation. You may choose to promote or denigrate certain products or services that have been targeted. This kind of commentary is known as "trash remark." Comment spam violates the interests of enterprises to a significant level, and it possesses a defensive mechanism that combines the "Deepfake" of artificial intelligence technology, which renders conventional security measures inapplicable. Deep learning is the approach that will be most effective in resolving this issue. Researchers assess this research according on how they extract features from the commentary dataset and how they extract features from the commentary dataset, employing a variety of methodologies and strategies to find solutions to the problems. The authors also looked at the key deep learning technologies that have been suggested as a solution to the issue of spam detection in the primary machine, as well as the performance of various deep learning technologies. At the same time, the fact that there is not a lot of spam online review data makes this sort of research a highly significant one. The purpose of this article is to offer an insightful and all-encompassing analysis of comparative research on the topic of spam detection that has recently been conducted.