Parametric Analysis for Fake Reviews Identification

Abstract

Online reviews are one of the most important aspects in a buyer's choice to buy a new product or use a service. As a result, it serves as a helpful source of data for determining public opinion regarding these products and services. It also provides companies with an indication of what kind of changes they need to make in their products to improve further. Thus, reviews also give competitors and product-based organizations a possible option to create fake reviews in order to advertise or degrade a product based on their interest. Hence, it is vital that the correct reviews are reached to the customers, and for this, the detection of fake ones is to be done effectively. In order to reduce the time for fake review detection, automated techniques are being used in the current scenario. Another concern is how to differentiate between the original and fake reviews. This paper discusses the various factors that can help in the identification of the same. They are broadly classified into two types: behavioral and feature-based. Also, the challenges that are still there in fake the review identification methods are depicted, and the open research areas where further work can be carried out are also being highlighted. The factors mentioned in the paper can prove useful for improvising the performance of any fake review detection system once applied to any real data set.