Scalability Issues in Authorship Attribution

The first in-depth and systematic study of the effects of author set size, data size, and the amount of topic variation, this account examines 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. Stressing the importance of methodology--even the seemingly insignificant decisions can drastically influence the outcome and reliability of an experiment--this analysis suggests a combined qualitative and quantitative evaluation of authorship attribution results. Scientists and scholars investigating large-scale applications of text analytics will be particularly interested in process's potential complications detailed herein.