Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry
In the e-commerce environment, it is very common for consumers to select goods or services based on online reviews from social platforms. However, the behavior of some unscrupulous merchants who hire a “water army” to brush up on reviews of their products has been continuously exposed, which serious...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
| Subjects: | |
| Online Access: | https://www.mdpi.com/0718-1876/18/1/6 |
| _version_ | 1797610707575898112 |
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| author | Jing Peng Yue Wang Yuan Meng |
| author_facet | Jing Peng Yue Wang Yuan Meng |
| author_sort | Jing Peng |
| collection | DOAJ |
| description | In the e-commerce environment, it is very common for consumers to select goods or services based on online reviews from social platforms. However, the behavior of some unscrupulous merchants who hire a “water army” to brush up on reviews of their products has been continuously exposed, which seriously misleads consumers’ purchasing decisions and undermines consumer trust. Until now, it has been a challenging task to accurately detect the “water army”, who could easily alter their behaviors or writing styles. The focus of this paper is on some collusive clues between members of the same social platform to propose a new graph model to detect the “water army”. First is the extraction of six kinds of user collusive relationships from two aspects: user content and user behavior. Further, the use of three aggregation methods on such collusive relationships generates a user collusive relationship factor (<b><i>CRF</i></b>), which is then used as the edge weight value in our graph-based water army detection model. In the combination of the graph grouping method and evaluation rules on candidate subgraphs, the graph model effectively detects multiple collusive groups automatically. The experimental results based on the Mafengwo platform show that the <b><i>CRF</i></b> generated from the coefficient of variation (CV) method demonstrates the best performance in detecting collusive groups, which provides some practical reference for the detection of “water armies” in an e-commerce environment. |
| first_indexed | 2024-03-11T06:19:00Z |
| format | Article |
| id | doaj.art-3adb07d557c64f95b3ecca75c09c7849 |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-03-11T06:19:00Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-3adb07d557c64f95b3ecca75c09c78492023-11-17T12:04:25ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762023-01-0118110512910.3390/jtaer18010006Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel IndustryJing Peng0Yue Wang1Yuan Meng2School of International Business, Zhejiang International Studies University, Hangzhou 310023, ChinaSchool of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, ChinaSchool of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, ChinaIn the e-commerce environment, it is very common for consumers to select goods or services based on online reviews from social platforms. However, the behavior of some unscrupulous merchants who hire a “water army” to brush up on reviews of their products has been continuously exposed, which seriously misleads consumers’ purchasing decisions and undermines consumer trust. Until now, it has been a challenging task to accurately detect the “water army”, who could easily alter their behaviors or writing styles. The focus of this paper is on some collusive clues between members of the same social platform to propose a new graph model to detect the “water army”. First is the extraction of six kinds of user collusive relationships from two aspects: user content and user behavior. Further, the use of three aggregation methods on such collusive relationships generates a user collusive relationship factor (<b><i>CRF</i></b>), which is then used as the edge weight value in our graph-based water army detection model. In the combination of the graph grouping method and evaluation rules on candidate subgraphs, the graph model effectively detects multiple collusive groups automatically. The experimental results based on the Mafengwo platform show that the <b><i>CRF</i></b> generated from the coefficient of variation (CV) method demonstrates the best performance in detecting collusive groups, which provides some practical reference for the detection of “water armies” in an e-commerce environment.https://www.mdpi.com/0718-1876/18/1/6water armywater army detectioncollusive relationshipgraph model |
| spellingShingle | Jing Peng Yue Wang Yuan Meng Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry Journal of Theoretical and Applied Electronic Commerce Research water army water army detection collusive relationship graph model |
| title | Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry |
| title_full | Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry |
| title_fullStr | Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry |
| title_full_unstemmed | Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry |
| title_short | Detecting E-Commerce Water Army through Graph Modeling on User Multiple Collusive Relationships: A Case Study of China’s Hotel Industry |
| title_sort | detecting e commerce water army through graph modeling on user multiple collusive relationships a case study of china s hotel industry |
| topic | water army water army detection collusive relationship graph model |
| url | https://www.mdpi.com/0718-1876/18/1/6 |
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