Detection of Fake Reviews Using Group Model
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Detection of Fake Reviews Using Group Model Yuejun Li 1,2,3 & Fangxin Wang 1,2 & Shuwu Zhang 1,2 & Xiaofei Niu 3 Accepted: 9 November 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Fake review detection . Opinion spamming . Review group detection . Reviewer group . Reviewer collusion
1 Introduction People have been active for years in online platform like amazon.com, eBay.com, taobao.com, yelp.com, dianping. com etc. and many of them are willing to share opinions about the product they buy or service they get. Meanwhile, some dishonest people write fake reviews just for economic reasons or get credit from the website. Some other people who we called “pseudo professional reviewer” may gather together via offline interaction or instant messaging software to complete a task assigned by store employers. These review spamming behaviors have badly influence customer purchase decisions [1] and the fame of the platform like yelp.com, dianping.com, amazon.com etc. * Xiaofei Niu [email protected] 1
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, Shandong, China
It’s a difficult thing to identify a review to be fake or not just by browsing the review list of products because fraudulent reviews are always mixed together when presenting by the website. Meanwhile
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