CSEIT183113 | Received : 01 Jan 2018 | Accepted : 12 Jan 2018 | January-February-2018 [(3) 1 : 71-76] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2018 IJSRCSEIT | Volume 3 | Issue 1 | ISSN : 2456-3307 71 A Survey on Challenges and Opportunistic Spotting Fake Reviewer Groups in Consumer Reviews P. Mrudula *1 , B. Sankara Babu 2 *1 M.Tech, CSE, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India 2 Professor, CSE, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India ABSTRACT Online customer reviews for both products and merchants have greatly affected others decision making in purchase. Considering the easily accessibility of the reviews and the significant impacts to the retailers, there is an increasing incentive to manipulate the reviews, mostly profit driven. Without proper protection, group spam reviews will cause gradual loss of credibility of the reviews and corrupt the entire online review systems eventually. Therefore, review spam detection is considered as the first step towards securing the online review systems. In this paper, aim to overview existing detection approaches in a systematic way, define key research issues, and articulate future research challenges and opportunities for group review spam detection. Keywords: Review Spam, Review Spammer, Spam Behavior. I. INTRODUCTION People's attitudes and opinions are highly influence able by others, which is known as the word-of-mouth effect in shaping decision making. The Internet and Web-based technologies have created vast opportunities to enable online word-of-mouth carriers that play a critical role in influencing consumer purchase decision in electronic commerce. People exchange opinions about products or merchants in online blogs, forums, social media, or directly post reviews in various reputation systems provided by individual online retailers, mega-retailers (e.g., eBay, Amazon), or third-party sites (e.g., Bizrate, resellerrating.com, Google+ Local, Yelp, etc.). Recent surveys show that 83% of the consumers check out online reviews to know about the products or businesses they are buying from [1], and 80% of the consumers have changed purchase decision due to negative reviews [2]. Given the user-generated nature of online reviews and the increasing impact on purchase decision making, the quality and credibility of the reviews becomes a primary concern. Many review sites allow consumers to rate products or stores, write detailed comments, or assess others' reviews (e.g., labeling as "helpful") to express individual opinions. The ratings and reviews are highly subjective and often individually biased. Moreover, with profit incentives, unreliable reviews with dishonest ratings, known as review spams, are intentionally inserted into online review systems, e.g., product manufacturers, competitors or professional online reputation management companies may fake false positive (a.k.a ballot stuffing) or maliciously negative reviews (a.k.a. bad mouthing) to promote or demote a product, or to attract customers to a store or distract them from competitors. Driven by profits, a large number of spam reviews inserted manually or automatically by professional review management companies have been observed on many well-known online reputation systems. The gradual loss of credibility of online reviews will keep confusing the consumers with poor or wrong assessment and eventually cause the corruption of the review system. Review spam detection is the first step towards securing the online review systems. In this paper, we aim to overview existing detection approaches in a systematic way, define key research issues, and articulate future research challenges for review spam detection.