Domain Independent Authorship Attribution without Domain Adaptation

Automatic authorship attribution, by its nature, is much more advantageous if it is domain (i.e., topic and/or genre) independent. That is, many real world problems that require authorship attribution may not have in-domain training data readily available. However, most previous work based on machine learning techniques focused only on in-domain text for authorship attribution. In this paper, we present comprehensive evaluation of various stylometric techniques for cross-domain authorship attribution. From the experiments based on the Project Gutenberg book archive, we discover that extremely simple techniques based on stopwords are surprisingly robust against domain change, essentially ridding the need for domain adaptation when supplied with a large amount of data.