Exploring linkability of user reviews

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Abstract

Large numbers of people all over the world read and contribute to various review sites. Many contributors are understandably concerned about privacy in general and, specifically, about linkability of their reviews (and accounts) across multiple review sites. In this paper, we study linkability of community-based reviewing and try to answer the question: to what extent are "anonymous" reviews linkable, i.e., highly likely authored by the same contributor? Based on a very large set of reviews from one very popular site (Yelp), we show that a high percentage of ostensibly anonymous reviews can be accurately linked to their authors. This is despite the fact that we use very simple models and equally simple features set. Our study suggests that contributors reliably expose their identities in reviews. This has important implications for cross-referencing accounts between different review sites. Also, techniques used in our study could be adopted by review sites to give contributors feedback about linkability of their reviews. © 2012 Springer-Verlag.

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APA

Almishari, M., & Tsudik, G. (2012). Exploring linkability of user reviews. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7459 LNCS, pp. 307–324). https://doi.org/10.1007/978-3-642-33167-1_18

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