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Using Data Compression to Build a Method for Statistically Verified Attribution of Literary Texts.

Boris Ryabko1,2, Nadezhda Savina2

  • 1Federal Research Center for Information and Computational Technologies of SB RAS, 630090 Novosibirsk, Russia.

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Summary
This summary is machine-generated.

This study introduces a novel authorship attribution method for literary texts using data compression techniques. This approach provides statistically verifiable results for analyzing Russian literature authorship.

Keywords:
authorship attribution of literary textsdata compressionhypothesis testingquantitative study of literature

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Area of Science:

  • Computational Linguistics
  • Literary Studies
  • Digital Humanities

Background:

  • Authorship attribution is crucial for literary analysis.
  • Traditional methods may lack statistical rigor.
  • Quantitative approaches offer new insights into literary studies.

Purpose of the Study:

  • To propose a new methodology for literary text authorship attribution.
  • To leverage data compression for statistically verified attribution.
  • To apply the method to specific problems in Russian literature.

Main Methods:

  • Utilizing data compressors as a core component of the attribution methodology.
  • Developing a quantitative framework for analyzing textual features.
  • Implementing statistical verification for attribution results.

Main Results:

  • Demonstrated the efficacy of data compression for authorship attribution.
  • Achieved statistically verified results in attribution tasks.
  • Successfully applied the method to two distinct Russian literature attribution problems.

Conclusions:

  • Data compression offers a robust and statistically sound method for authorship attribution.
  • The proposed methodology advances quantitative literary studies.
  • This approach has significant potential for literary scholarship, particularly in analyzing Russian literary works.