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A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content

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  • Matthew J. Schneider

    (Drexel University)

  • Shawn Mankad

    (Cornell University)

Abstract

User-generated content (UGC) is an important source of information on products and services for consumers and firms. Although incentivizing high-quality UGC is an important business objective for any content platform, we show that it is also possible to identify anonymous posters by exploiting the characteristics of posted content. We present a novel two-stage authorship attribution methodology that combines structured and text data by identifying an author first by the amount and granularity of structured data (e.g., location, first name) posted with the UGC and second by the author’s writing style. As a case study, we show that 75% of the 1.3 million users in data publicly released by Yelp are uniquely identified by three structured variable combinations. For the remaining 25%, when the number of potential authors with (nearly) identically structured data ranges from 100 to 5 and sufficient training data exists for text analysis, the average probabilities of identification range from 40 to 81%. Our findings suggest that UGC platforms concerned with the potential negative effects of privacy-related incidents should limit or generalize their posters’ structured data when it is adjoined with textual content or mentioned in the text itself. We also show that although protection policies that focus on structured data remove the most predictive elements of authorship, they also have a small negative effect on the usefulness of content.

Suggested Citation

  • Matthew J. Schneider & Shawn Mankad, 2021. "A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(3), pages 66-83, September.
    • Handle: RePEc:spr:custns:v:8:y:2021:i:3:d:10.1007_s40547-021-00116-x
    DOI: 10.1007/s40547-021-00116-x
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    1. V. Kumar & Werner Reinartz, 2018. "Customer Privacy Concerns and Privacy Protective Responses," Springer Texts in Business and Economics, in: Customer Relationship Management, edition 3, chapter 14, pages 285-309, Springer.
    2. Kelly D. Martin & Patrick E. Murphy, 2017. "The role of data privacy in marketing," Journal of the Academy of Marketing Science, Springer, vol. 45(2), pages 135-155, March.
    3. Xiao-Bai Li & Jialun Qin, 2017. "Anonymizing and Sharing Medical Text Records," Information Systems Research, INFORMS, vol. 28(2), pages 332-352, June.
    4. Jean-Charles Rochet & Jean Tirole, 2003. "Platform Competition in Two-Sided Markets," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 990-1029, June.
    5. V. Kumar & Werner Reinartz, 2018. "Customer Relationship Management," Springer Texts in Business and Economics, Springer, edition 3, number 978-3-662-55381-7, June.
    6. Moshe Koppel & Jonathan Schler & Shlomo Argamon, 2009. "Computational methods in authorship attribution," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 9-26, January.
    7. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    8. Matthew J. Schneider & Sharan Jagpal & Sachin Gupta & Shaobo Li & Yan Yu, 2018. "A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data," Marketing Science, INFORMS, vol. 37(1), pages 153-171, January.
    9. Davide Proserpio & Georgios Zervas, 2017. "Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews," Marketing Science, INFORMS, vol. 36(5), pages 645-665, September.
    10. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    11. Shawn Mankad & Hyunjeong Spring Han & Joel Goh & Srinagesh Gavirneni, 2016. "Understanding Online Hotel Reviews Through Automated Text Analysis," Post-Print hal-02311939, HAL.
    12. Schneider, Matthew J. & Jagpal, Sharan & Gupta, Sachin & Li, Shaobo & Yu, Yan, 2017. "Protecting customer privacy when marketing with second-party data," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 593-603.
    13. Jie Xu & Min Ding, 2019. "Using the Double Transparency of Autonomous Vehicles to Increase Fairness and Social Welfare," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 6(1), pages 26-35, June.
    14. Quentin André & Ziv Carmon & Klaus Wertenbroch & Alia Crum & Douglas Frank & William Goldstein & Joel Huber & Leaf Boven & Bernd Weber & Haiyang Yang, 2018. "Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 28-37, March.
    15. Zhang, Yuchi & Moe, Wendy W. & Schweidel, David A., 2017. "Modeling the role of message content and influencers in social media rebroadcasting," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 100-119.
    16. James Campbell & Avi Goldfarb & Catherine Tucker, 2015. "Privacy Regulation and Market Structure," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 24(1), pages 47-73, March.
    17. Xiao-Bai Li & Sumit Sarkar, 2006. "Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data," Information Systems Research, INFORMS, vol. 17(3), pages 254-270, September.
    18. Efstathios Stamatatos, 2009. "A survey of modern authorship attribution methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 538-556, March.
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