A Survey for Efficient and Trustworthy Methods for Detection of Dishonest Recommenders in OSN

Abstract Nowadays online social network (OSN) has become crucial part of viral marketing. Some of the popular OSNs can be named as Facebook, LikedIn, Twitter, Flixter in China, etc. Advancement in technology has led to integration of smart phones with OSNs. Using this, many users share their likes, dislikes, opinions, information with their friends in their network. Due to huge popularity of OSN, companies are using target oriented advertisement i. e. viral marketing to publicize or increase the sale of their products. Companies attract small group of users in an OSN and these users provide recommendation to their friends which increases the overall sales of the given product. This also gives chance to spread misleading recommendations to the friends. Therefore, accurate identification of dishonest, misleading users is necessary. Here, glimpse of approaches to detect malicious behaviors of users in OSN, online rating systems, spam detection etc. and their effect on viral marketing have been focused