M.Phil / MS

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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.
A Study on Diversification of Online Product Reviews
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Abid Ali; FA17-RCS-034; LHR TP 5982; Dr. Hamid Turab Mirza
Recent studies have boosted the e¬commerce industry which has resulted in increased significance of online product reviews. However, this usefulness of product reviews has also attracted the people who try to manipulate overall product perception by generating fake reviews. Another challenge due to boost in e¬commerce is the information overload which is caused by generation of huge reviews data. This study paves a complete pathway by presenting techniques for removal of spam reviews and by proposing a novel algorithm to retrieve a diversified subset of reviews to reduce the burden of information overload. A diversified set of reviews attempts to cover maximum features of the selected product within a limited number of reviews that ultimately leads to reduction in decision time as well as enhances the credibility and reliability for the user. Spam detection techniques were formulated based on deep learning models whereas novel SENTIMENT AND FEATURE ORIENTED DIVERSIFICATION (SeFOD) algorithm was constructed on the features addressed in each review and the sentiments of the review separately. The proposed models showed prominent results and achieved a maximum spam accuracy of 95.78%, 96.38% and 96.18% for LSTM, GRU and CNN models re spectively. The same results were validated on Yelp hotel reviews dataset. Whereas a new measure for calculating the diversity of the reviews set was adopted named as DivScore. The score nearer to 0 means there is no diversity in the set and hence all the retrieved reviews contain similar features. The far this score goes from 0, the more diversity exists in the diversified set. A DivScore of 7.14 was achieved for the selected product from Daraz reviews dataset while 10.88 was the score when a product was diversified from Yelp reviews dataset. This study can be used by e¬commerce industry to maximize their profits as well as is equally relevant for the general users to better choose relevant product for them

M.Phil / MS

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