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Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regression

Author

Listed:
  • Cuixia Jiang

    (Hefei University of Technology)

  • Jun Zhu

    (Hefei University of Technology)

  • Qifa Xu

    (Hefei University of Technology)

Abstract

Click farming has become a common phenomenon, which brings great harm to the online shopping platform and consumers. To identify click farming on the Taobao platform, the largest online shopping platform in China, we use the positive-unlabeled learning method to find reliable negative instances from the unlabeled set and output the identification of click farming with probability rank for all shops, after creating several features from both goods and online shops. Then, a weighted logit model is used to investigate the role of extracted features in dissecting click farming. The empirical findings show that the extracted features are efficient to identify and explain click farming. And, the results show that click farming may not necessarily depend on the state of the shop. Our study can help online consumers to reduce the risk of being deceived, and help the platform to improve its regulatory capacity in click farming.

Suggested Citation

  • Cuixia Jiang & Jun Zhu & Qifa Xu, 2022. "Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regression," Electronic Commerce Research, Springer, vol. 22(1), pages 157-176, March.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:1:d:10.1007_s10660-020-09418-z
    DOI: 10.1007/s10660-020-09418-z
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    References listed on IDEAS

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    1. Qihua Liu & Shan Huang & Liyi Zhang, 2016. "The influence of information cascades on online purchase behaviors of search and experience products," Electronic Commerce Research, Springer, vol. 16(4), pages 553-580, December.
    2. Min Chen & Varghese S. Jacob & Suresh Radhakrishnan & Young U. Ryu, 2015. "Can Payment-per-Click Induce Improvements in Click Fraud Identification Technologies?," Information Systems Research, INFORMS, vol. 26(4), pages 754-772, December.
    3. Wessel, Michael & Thies, Ferdinand & Benlian, Alexander, 2016. "The Emergence and Effects of Fake Social Information: Evidence from Crowdfunding," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 82421, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Hou, Jingrui & Chi, Ming & Li, Tao & Guan, Zhi-Hong & Luo, Kai & Zhang, Ding-Xue, 2019. "Spreading dynamics of SVFR online fraud information model on heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    5. Zhang, Chaowei & Gupta, Ashish & Kauten, Christian & Deokar, Amit V. & Qin, Xiao, 2019. "Detecting fake news for reducing misinformation risks using analytics approaches," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1036-1052.
    6. Theodoros Lappas & Gaurav Sabnis & Georgios Valkanas, 2016. "The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry," Information Systems Research, INFORMS, vol. 27(4), pages 940-961, December.
    7. Vikramaditya Khanna & E. Han Kim & Yao Lu, 2015. "CEO Connectedness and Corporate Fraud," Journal of Finance, American Finance Association, vol. 70(3), pages 1203-1252, June.
    8. Wessel, Michael & Thies, Ferdinand & Benlian, Alexander, 2016. "The Emergence and Effects of Fake Social Information: Evidence from Crowdfunding," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 83005, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Muhammad Rifki Shihab & Audry Pragita Putri, 2019. "Negative online reviews of popular products: understanding the effects of review proportion and quality on consumers’ attitude and intention to buy," Electronic Commerce Research, Springer, vol. 19(1), pages 159-187, March.
    10. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    11. Subhasis Thakur, 2019. "A reputation management mechanism that incorporates accountability in online ratings," Electronic Commerce Research, Springer, vol. 19(1), pages 23-57, March.
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    1. Chen, Yujing & Zhong, Yuanguang & Cheng, T.C.E., 2023. "Impacts of the minimum quantity contract on an online retail platform," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1236-1247.

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