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Related Experiment Video

Updated: Mar 8, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

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Published on: March 21, 2019

Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks.

Camilo Akimushkin¹, Diego Raphael Amancio², Osvaldo Novais Oliveira¹

¹São Carlos Institute of Physics, University of São Paulo, São Carlos, São Paulo, Brazil.

|January 27, 2017

Summary

This summary is machine-generated.

Authorship attribution is now possible using dynamic word co-occurrence networks. This method analyzes network metric fluctuations to identify stylistic features, achieving an 88.75% success rate in classifying texts by author.

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Area of Science:

Computational linguistics
Network science
Stylometry

Background:

Complex network theory aids authorship identification without semantic knowledge.
Previous studies focused on large text networks; suitability for small text chunks was less explored.

Purpose of the Study:

Introduce a methodology using word co-occurrence network dynamics for text classification.
Assess the effectiveness of network metrics in capturing authorship stylistic features.

Main Methods:

Texts were segmented into equal linguistic token sections.
Time series of 12 network topological metrics were generated.
Distribution moments of stationary/integrable network metrics were used as features.

Main Results:

A supervised learning procedure with Isomap nonlinear transformation and K-nearest neighbors was employed.
71 out of 80 texts (88.75%) were correctly classified by author.
Dynamic fluctuations in network metrics proved effective for authorship characterization.

Conclusions:

Dynamic network metrics offer a robust method for authorship attribution.
This approach enables the description of large texts using small, evolving networks.