Review Spam Detection by Highlighting Potential Spammers and Diminishing Their Effect

Nowadays, millions of products and services are available to the public online. Therefore, searching for the best products which meets individuals' expectations would be difficult due to the existence of too many alternative choices. One of the most reliable approaches to choose a product or service is to exploit the experience of people who have already tried them, and are expected to have reported their almost honest opinions about them. A reviewing system is a place where individuals share their experience on products and services. Individuals may read and/or write their reviews which may be neutral and professional or biased. Moreover, companies utilize reviewing systems to apply opinion mining techniques in order to improve their goods or services and may be to watch their competitors. However, the popularity of reviewing systems ignites this motivation for some people to try to influence viewers by entering their fake reviews to promote some products or defame some others. These spam reviews should be detected and eliminated to prevent misleading potential customers and unethically affect the market. Opinion mining should be adapted to locate and eliminate potential spam reviews. In this paper, some review spam detection approaches have been proposed and examined over a sample dataset. The proposed approaches consider patterns that existed in trends of reviews, as well as reviewers' behavior. The approaches depend on various strategies such as observing abnormal trends, detecting uncommon or suspicious behaviors, investigating group activities, among others. The reported test results revealed some promising outcome.