On the feasibility of internet-scale author identification

Arvind Narayanan, Hristo Paskov, Neil Zhenqiang Gong, John Bethencourt, Emil Stefanov, Eui Chul Richard Shin, Dawn Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

147 Scopus citations

Abstract

We study techniques for identifying an anonymous author via linguistic stylometry, i.e., comparing the writing style against a corpus of texts of known authorship. We experimentally demonstrate the effectiveness of our techniques with as many as 100,000 candidate authors. Given the increasing availability of writing samples online, our result has serious implications for anonymity and free speech - an anonymous blogger or whistleblower may be unmasked unless they take steps to obfuscate their writing style. While there is a huge body of literature on authorship recognition based on writing style, almost none of it has studied corpora of more than a few hundred authors. The problem becomes qualitatively different at a large scale, as we show, and techniques from prior work fail to scale, both in terms of accuracy and performance. We study a variety of classifiers, both "lazy" and "eager," and show how to handle the huge number of classes. We also develop novel techniques for confidence estimation of classifier outputs. Finally, we demonstrate stylometric authorship recognition on texts written in different contexts. In over 20% of cases, our classifiers can correctly identify an anonymous author given a corpus of texts from 100,000 authors; in about 35% of cases the correct author is one of the top 20 guesses. If we allow the classifier the option of not making a guess, via confidence estimation we are able to increase the precision of the top guess from 20% to over 80% with only a halving of recall.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 IEEE Symposium on Security and Privacy, S and P 2012
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages300-314
Number of pages15
ISBN (Print)9780769546810
DOIs
StatePublished - 2012
Event33rd IEEE Symposium on Security and Privacy, S and P 2012 - San Francisco, CA, United States
Duration: May 21 2012May 23 2012

Publication series

NameProceedings - IEEE Symposium on Security and Privacy
ISSN (Print)1081-6011

Other

Other33rd IEEE Symposium on Security and Privacy, S and P 2012
CountryUnited States
CitySan Francisco, CA
Period5/21/125/23/12

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Software
  • Computer Networks and Communications

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