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


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.

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    As reviewed in Shu et al. [57], this problem is known by a number of names, including User Identity Linkage, Social Identity Linkage, User Identity Resolution, Social Network Reconciliation, User Account Linkage Inference, Profile Linkage, Anchor Link Prediction, and Detecting me edges.

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    We explored several different kernels for SVM, including polynomial (2nd and 3rd order) and other nonlinear specifications. A linear kernel achieved the best results and is therefore presented throughout the paper.


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We are thankful to Elea Feit, Sachin Gupta, Cameron Bale, and Sharan Jagpal for their helpful comments on earlier versions of this paper.

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Correspondence to Matthew J. Schneider.

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The authors declare no competing interests.

Appendix. Expanded results for the yelp data

Appendix. Expanded results for the yelp data

Figure 4 provides out-of-sample accuracy results for the Yelp data. Accuracy consistently improves as the sophistication of the data intruder and the size of the training data increase.

Fig. 4

Full second stage accuracy results for the Yelp dataset

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Schneider, M.J., Mankad, S. A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content. Cust. Need. and Solut. 8, 66–83 (2021).

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  • Data privacy
  • De-anonymization
  • Stylometry
  • User identity linkage
  • Data linkage