(untitled) (International Journal of Engineering and Computer Science, 2017)
In the era of web 2.0, huge volumes of consumer reviews are posted to the internet every day. As the access to Internet has been so much easier, there is an increase in people using online applications more than ever. Online marketing, in fact, the whole e-commerce is getting enormous day by day if not in every minute. Online reviews play a very important role in this field and proved itself to be auspicious in terms of decision making from a customer’s point of view. Customers use the reviews for deciding quality of products before purchasing them. Companies or vendors use opinions to take a decision to improve their sales according to intelligent things done by other competitors. However, all reviews given by customers or users are not true reviews. Manual approaches to detecting and analyzing fake reviews are not practical due to the problem of information overload. The design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews. As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. The main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. Hence, a novel approach, distance based outlier detection methods namely Cooks distance and Mahanabolis distance is used to identify spam reviews. M