Which goods are most likely to be subject to click farming? An evidence from the Taobao platform
•We conduct an empirical analysis of click farming on the Taobao platform in China.•We extract several new features with our own experience in click farming.•We identify click farming and compare its behavior on different categories of goods.•We conduct importance analysis and partial dependence ana...
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| Published in | Electronic commerce research and applications Vol. 50; p. 101107 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.11.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | •We conduct an empirical analysis of click farming on the Taobao platform in China.•We extract several new features with our own experience in click farming.•We identify click farming and compare its behavior on different categories of goods.•We conduct importance analysis and partial dependence analysis to do further study.•The results confirm the effectiveness of our features in identifying click farming.
Click farming is common in online shopping. It is thus important to identify click farming and compare its performance across different categories of online goods. To this end, we conduct an empirical analysis of click farming on the Taobao platform in China. First, we extract several new features from three sources, namely main goods, online shop itself, and online reviews, based on the formation mechanism of click farming. Second, we investigate their usefulness in identifying click farming among different online goods, including importance analysis and partial dependence analysis. Third, we further investigate the contribution of constructed features to predicting click farming. Our findings confirm the effectiveness of our created features and the heterogeneity of click farming among different online goods. Specifically, click farming is most likely to happen in clothing-related goods, then followed by electronic goods and service-related goods. Our results are significant for consumers to understand online information and for online business platforms to reduce the occurrence of click farming. |
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| AbstractList | •We conduct an empirical analysis of click farming on the Taobao platform in China.•We extract several new features with our own experience in click farming.•We identify click farming and compare its behavior on different categories of goods.•We conduct importance analysis and partial dependence analysis to do further study.•The results confirm the effectiveness of our features in identifying click farming.
Click farming is common in online shopping. It is thus important to identify click farming and compare its performance across different categories of online goods. To this end, we conduct an empirical analysis of click farming on the Taobao platform in China. First, we extract several new features from three sources, namely main goods, online shop itself, and online reviews, based on the formation mechanism of click farming. Second, we investigate their usefulness in identifying click farming among different online goods, including importance analysis and partial dependence analysis. Third, we further investigate the contribution of constructed features to predicting click farming. Our findings confirm the effectiveness of our created features and the heterogeneity of click farming among different online goods. Specifically, click farming is most likely to happen in clothing-related goods, then followed by electronic goods and service-related goods. Our results are significant for consumers to understand online information and for online business platforms to reduce the occurrence of click farming. |
| ArticleNumber | 101107 |
| Author | Zhu, Jun Jiang, Cuixia Xu, Qifa |
| Author_xml | – sequence: 1 givenname: Cuixia surname: Jiang fullname: Jiang, Cuixia organization: School of Management, Hefei University of Technology, Hefei 230009, China – sequence: 2 givenname: Jun surname: Zhu fullname: Zhu, Jun organization: School of Management, Hefei University of Technology, Hefei 230009, China – sequence: 3 givenname: Qifa surname: Xu fullname: Xu, Qifa email: xuqifa@hfut.edu.cn organization: School of Management, Hefei University of Technology, Hefei 230009, China |
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| Keywords | Weighted random forest Online goods PU learning Click farming Taobao |
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| SubjectTerms | Click farming Online goods PU learning Taobao Weighted random forest |
| Title | Which goods are most likely to be subject to click farming? An evidence from the Taobao platform |
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