Browsing by Author "SP17-RCS-017"

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    Group Spam identification in Online Product Reviews
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) GHULAM MEHMOOD; SP17-RCS-017; LHR TP 6400; Dr. Hamid Turab Mirza
    In this era of e-commerce, user’s opinion about a product on the online review system is of great importance, as it provides guidance for people to decide. Being very important, people used to write fake reviews about the products, called opinion spamming. Detecting opinion spams in online review platforms is a challenging task drawing attention from research communities. It is a persistent campaign between the spammers and platforms. Grouped opinion spamming is the main type of opinion spamming in the online review system these days. For that purpose, usually people make multiple accounts to write fake reviews or they pay to crowdsourcing platforms to write fake reviews for promotion of their product or demotion of their competitor’s product. Group spam reviews are more damaging for online review systems as reviews from many peoples about a product either it is positive, or negative can easily deceive peoples as compared to a single spam review. These spam review groups should be detected so that reviews about that product reflect genuine user opinion. Many researchers try to resolve this problem using behavioral and linguistic features of the users and reviews. Many machine learning models are being adapted to solve this problem but could not resolve this problem completely. The purpose of this research work is to design a framework that detect group spammer who targets online review systems. This framework has used linguistic, behavioral, and structural features to dig out all such spammer groups who write fake reviews. First, constructed a reviewer-product network between reviewers and products. Then written an algorithm to find out collusiveness score between reviewers who have commonly reviewed a product and constructed a network between reviewers. Then all those edges (reviewer pairs) whose score was below a threshold valued was removed from the network. After that extracted high quality candidate spammer groups by using a novel algorithm. Five best group spam indicators are used to calculate spamicity score of candidate spammer groups. The candidate groups whose spamicity score was greater than a threshold values (says 0.6) was considered as spammer groups. For the evaluation of proposed algorithm, experiments performed on 3 labeled datasets from Yelp. The system extracted spammer groups from online reviews with precision of 0.89 @ top 50 spammer groups.