Authorship recognition via fluctuation analysis of network topology and word intermittency

TL;DR

This paper explores how stylistic fluctuations in texts, analyzed through complex network topology and word intermittency, can be used to identify authorship and improve NLP tasks.

Contribution

It introduces a novel approach using fluctuation analysis of network topology and word intermittency for authorship recognition.

Findings

Distinct fluctuation patterns are associated with different authors.

Intermittency of specific words can reliably identify authorship.

Stylistic fluctuation patterns have potential applications in NLP.

Abstract

Statistical methods have been widely employed in many practical natural language processing applications. More specifically, complex networks concepts and methods from dynamical systems theory have been successfully applied to recognize stylistic patterns in written texts. Despite the large amount of studies devoted to represent texts with physical models, only a few studies have assessed the relevance of attributes derived from the analysis of stylistic fluctuations. Because fluctuations represent a pivotal factor for characterizing a myriad of real systems, this study focused on the analysis of the properties of stylistic fluctuations in texts via topological analysis of complex networks and intermittency measurements. The results showed that different authors display distinct fluctuation patterns. In particular, it was found that it is possible to identify the authorship of books using the intermittency of specific words. Taken together, the results described here suggest that the patterns found in stylistic fluctuations could be used to analyze other related complex systems. Furthermore, the discovery of novel patterns related to textual stylistic fluctuations indicates that these patterns could be useful to improve the state of the art of many stylistic-based natural language processing tasks.