Text authorship identified using the dynamics of word co-occurrence networks

TL;DR

This paper presents a novel method for authorship attribution using the dynamics of word co-occurrence networks, achieving high accuracy in classifying texts by authors through network metric analysis and machine learning.

Contribution

The study introduces a new network-based approach for authorship identification that leverages the dynamics of co-occurrence networks and supervised learning, improving classification efficiency.

Findings

85% correct classification rate on 80 texts

Stationarity of network metric time series validated

Dynamic network metrics effectively characterize authorship

Abstract

The identification of authorship in disputed documents still requires human expertise, which is now unfeasible for many tasks owing to the large volumes of text and authors in practical applications. In this study, we introduce a methodology based on the dynamics of word co-occurrence networks representing written texts to classify a corpus of 80 texts by 8 authors. The texts were divided into sections with equal number of linguistic tokens, from which time series were created for 12 topological metrics. The series were proven to be stationary (p-value>0.05), which permits to use distribution moments as learning attributes. With an optimized supervised learning procedure using a Radial Basis Function Network, 68 out of 80 texts were correctly classified, i.e. a remarkable 85% author matching success rate. Therefore, fluctuations in purely dynamic network metrics were found to characterize authorship, thus opening the way for the description of texts in terms of small evolving networks. Moreover, the approach introduced allows for comparison of texts with diverse characteristics in a simple, fast fashion.