Authorship Attribution: Comparison of Single-Layer and Double-Layer Machine Learning

Abstract

In the traditional authorship attribution task, forensic linguistic specialists analyse and compare documents to determine who was their (real) author. In the current days, the number of anonymous documents is growing ceaselessly because of Internet expansion. That is why the manual part of the authorship attribution process needs to be replaced with automatic methods. Specialized algorithms (SA) like delta-score and word length statistic were developed to quantify the similarity between documents, but currently prevailing techniques build upon the machine learning (ML) approach.

In this paper, two machine learning approaches are compared: Single-layer ML, where the results of SA (similarities of documents) are used as input attributes for the machine learning, and Double-layer ML with the numerical information characterizing the author being extracted from documents and divided into several groups. For each group the machine learning classifier is trained and the outputs of these classifiers are used as input attributes for ML in the second step.

Generating attributes for the machine learning in the first step of double-layer ML, which is based on SA, is described in detail here. Documents from Czech blog servers are utilized for empirical evaluation of both approaches.