Scalability Issues in Authorship Attribution

Book excerpt: Provides an in-depth and systematic study of the so-called scalability issues in authorship attribution -- the task that aims to identify the author of a text, given a model of authorial style based on texts of known authorship. Computational authorship attribution does not rely on in-depth reading, but rather automates the process. This book investigates the behavior of a text categorization approach to the task when confronted with scalability issues. By addressing the issues of experimental design, data size, and author set size, the dissertation demonstrates whether the approach taken is valid in experiments with limited or sufficient data, and with small or large sets of authors.