Detection of Fake Reviews Using Group Model
文献类型：期刊论文
| 作者 | Li, Yuejun1,2,3; Wang, Fangxin2,3 ; Zhang, Shuwu2,3; Niu, Xiaofei1
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| 刊名 | MOBILE NETWORKS & APPLICATIONS
 |
| 出版日期 | 2020-11-25 |
| 页码 | 13 |
| 关键词 | Fake review detection Opinion spamming Review group detection Reviewer group Reviewer collusion |
| ISSN号 | 1383-469X |
| DOI | 10.1007/s11036-020-01688-z |
| 通讯作者 | Niu, Xiaofei(niuxiaofei2002@163.com) |
| 英文摘要 | 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. |
| 资助项目 | National Key R&D Program of China[2017YFB1401000] ; Key Laboratory of Digital Rights Services of the National Science and Standardization Key Labs for Press and Publication Industry, National Natural Science Foundation of China[61672328] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000592556300002 |
| 出版者 | SPRINGER |
| 资助机构 | National Key R&D Program of China ; Key Laboratory of Digital Rights Services of the National Science and Standardization Key Labs for Press and Publication Industry, National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/41669]  |
| 专题 | 数字内容技术与服务研究中心_新媒体服务与管理技术 |
| 通讯作者 | Niu, Xiaofei |
| 作者单位 | 1.Shandong Jianzhu Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yuejun,Wang, Fangxin,Zhang, Shuwu,et al. Detection of Fake Reviews Using Group Model[J]. MOBILE NETWORKS & APPLICATIONS,2020:13. |
| APA | Li, Yuejun,Wang, Fangxin,Zhang, Shuwu,&Niu, Xiaofei.(2020).Detection of Fake Reviews Using Group Model.MOBILE NETWORKS & APPLICATIONS,13. |
| MLA | Li, Yuejun,et al."Detection of Fake Reviews Using Group Model".MOBILE NETWORKS & APPLICATIONS (2020):13. |
入库方式： OAI收割
来源：自动化研究所
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