Authorship Attribution Based on Life-Like Network Automata

TL;DR

This paper introduces a novel authorship attribution method using life-like network automata, combining topological and dynamical network features, outperforming traditional methods and highlighting the importance of pre-processing steps.

Contribution

The paper proposes a new approach that integrates cellular automata concepts into text network analysis for improved authorship attribution.

Findings

Outperforms traditional topological measurement methods.

Pre-processing steps significantly influence results.

Dynamical network features enhance attribution accuracy.

Abstract

The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over traditional analysis relying only on topological measurements. Remarkably, we have found a dependence of pre-processing steps (such as the lemmatization) on the obtained results, a feature that has mostly been disregarded in related works. The optimized results obtained here pave the way for a better characterization of textual networks.