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.