An algorithm for automated authorship attribution using neural networks

Abstract

We present an algorithm as evidence of the possibility of a truly automated stylometric authorship attribution tool, based on committees of artificial neural networks. Neural networks have an advantage over traditional statistical stylometry in that they are inherently nonlinear, and therefore can consider nonlinear interactions between stylometric variables. The algorithm presented (1) is intended to demonstrate the feasibility of an automated approach using neural networks and (2) highlights important areas for further research. We present results of two separate test experiments-Shakespeare and Marlowe, and the Federalist Papers-as a demonstration of the method's generality. In both cases, our algorithm produces committees that correctly predict the test works, without requiring the usual precursory statistical study to determine efficacious stylometric measures.