Unsupervised authorship attribution

2015/03/26 by David A. Fifield, Fifield, David, Torbjørn Follan +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Authorship Attribution and Profiling #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1503.07613

openalex publication_date 2015/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We describe a technique for attributing parts of a written text to a set of unknown authors. Nothing is assumed to be known a priori about the writing styles of potential authors. We use multiple independent clusterings of an input text to identify parts that are similar and dissimilar to one another. We describe algorithms necessary to combine the multiple clusterings into a meaningful output. We show results of the application of the technique on texts having multiple writing styles.

Citations

• Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming

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