Detection of Fake Reviews Using Group Model

Reviews of product or stores exist extensively in online e-commerce platform which is important for customers to make decisions. For economic reasons some dishonest people are employed to write fake reviews which is also called opinion spamming to promote or demote target products and services. Previous researches have made use of text similarity, linguistics, rating patterns, graph relationship and other behaviors for spammer detection. They mainly utilized product review list while it is difficult to find fake reviews by glancing over product reviews in time-descending order. Meanwhile there exists lots of useful information in the list of reviews of reviewers and relationships between reviewers when reviewers commonly reviewed the same stores. We propose the concept of review group and to the best of our knowledge, it's the first time the review group concept is proposed and used. Review grouping algorithm is designed to effectively split reviews of reviewer into groups which participate in building novel grouping models to identify both positive and negative deceptive reviews. Several new features which are language independent based on group model are constructed. Additionally, we explore the collusion relationship between reviewers to build reviewer group collusion model. Evaluations show that the review group method and reviewer group collusion models can effectively improve the precision by 4%-7% compared to the baselines in fake reviews classification task especially when reviews are posted by professional review spammers.

Automated summary

Reviews of products and stores play a crucial role in online e-commerce platforms for customers to make decisions. Some dishonest individuals are hired to write fake reviews, known as opinion spamming, to manipulate the perception of target products or services. Previous research has focused on detecting fake reviews using various methods, but this study introduces the concept of review groups and explores the relationships between reviewers to improve the accuracy of identifying deceptive reviews through grouping algorithms and collusion models.