IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v22y2022i1d10.1007_s10660-020-09418-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09418-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-020-09418-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    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) 83005, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Subhasis Thakur, 2019. "A reputation management mechanism that incorporates accountability in online ratings," Electronic Commerce Research, Springer, vol. 19(1), pages 23-57, March.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    2. Moon, Sangkil & Kim, Moon-Yong & Bergey, Paul K., 2019. "Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms," Journal of Business Research, Elsevier, vol. 102(C), pages 83-96.
    3. Plotkina, Daria & Munzel, Andreas & Pallud, Jessie, 2020. "Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews," Journal of Business Research, Elsevier, vol. 109(C), pages 511-523.
    4. Chen Jin & Luyi Yang & Kartik Hosanagar, 2023. "To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders," Information Systems Research, INFORMS, vol. 34(2), pages 532-552, June.
    5. Moon, Sangkil & Kim, Moon-Yong & Iacobucci, Dawn, 2021. "Content analysis of fake consumer reviews by survey-based text categorization," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 343-364.
    6. Chen Jin & Luyi Yang & Kartik Hosanagar, 2019. "To Brush or Not to Brush: Product Rankings, Customer Search, and Fake Orders," Working Papers 19-02, NET Institute.
    7. Ferdinand Thies & Sören Wallbach & Michael Wessel & Markus Besler & Alexander Benlian, 2022. "Initial coin offerings and the cryptocurrency hype - the moderating role of exogenous and endogenous signals," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1691-1705, September.
    8. Ajay Kumar & Ram D. Gopal & Ravi Shankar & Kim Hua Tan, 2022. "Fraudulent review detection model focusing on emotional expressions and explicit aspects : investigating the potential of feature engineering," Post-Print hal-03630420, HAL.
    9. Thomas Niemand & Sascha Kraus & Martin Angerer & Ferdinand Thies & Alicia Mas-Tur, 2019. "More is not always better—non-linear effects in crowdfunding," International Journal of Quality Innovation, Springer, vol. 5(1), pages 1-10, December.
    10. Rob Gleasure & Kieran Conboy & Lorraine Morgan, 2019. "Talking Up a Storm: How Backers Use Public Discourse to Exert Control in Crowdfunded Systems Development Projects," Information Systems Research, INFORMS, vol. 30(2), pages 447-465, June.
    11. Thomas Clauss & Thomas Niemand & Sascha Kraus & Patrick Schnetzer & Alexander Brem, 2019. "Increasing Crowdfunding Success Through Social Media: The Importance Of Reach And Utilisation In Reward-Based Crowdfunding," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 24(03), pages 1-30, May.
    12. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    13. Arcuri, Maria Cristina & Gandolfi, Gino & Russo, Ivan, 2023. "Does fake news impact stock returns? Evidence from US and EU stock markets," Journal of Economics and Business, Elsevier, vol. 125.
    14. Mingchun Chen & Zhiying Liu & Chaoliang Ma & Xiuyuan Gong, 2021. "A distinctive early bird price in reward-based crowdfunding," Electronic Commerce Research, Springer, vol. 21(2), pages 347-370, June.
    15. Hung-Pin Shih & Pei-Chen Sung, 2021. "Addressing the Review-Based Learning and Private Information Approaches to Foster Platform Continuance," Information Systems Frontiers, Springer, vol. 23(3), pages 649-661, June.
    16. Martin Adam & Michael Wessel & Alexander Benlian, 2019. "Of early birds and phantoms: how sold-out discounts impact entrepreneurial success in reward-based crowdfunding," Review of Managerial Science, Springer, vol. 13(3), pages 545-560, June.
    17. Ku, Hsuan-Hsuan & Shang, Rong-An & Fu, Yi-Fan, 2021. "Social learning effects of complaint handling on social media: Self-construal as a moderator," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    18. Xiaohui Zhang & Qianzhou Du & Zhongju Zhang, 2022. "A theory‐driven machine learning system for financial disinformation detection," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3160-3179, August.
    19. Tanja Jovanović, 2019. "Crowdfunding: What Do We Know So Far?," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-25, February.
    20. Haichao Zheng & Zihao Qi & Xin Luo & Liting Li & Bo Xu, 2020. "The value of backers’ word-of-mouth in crowdfunding projects filtering: an empirical investigation," Electronic Commerce Research, Springer, vol. 20(4), pages 757-782, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:22:y:2022:i:1:d:10.1007_s10660-020-09418-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.