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

Automatic identification of authorship in disputed documents has benefited from complex network theory as this approach does not require human expertise or detailed semantic knowledge. Networks modeling entire books can be used to discriminate texts from different sources and understand network grow...

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Bibliographic Details
Published in:	PloS one 2017-01, Vol.12 (1), p.e0170527-e0170527
Main Authors:	Akimushkin, Camilo, Amancio, Diego Raphael, Oliveira, Jr, Osvaldo Novais
Format:	Article
Language:	eng
Subjects:	Algorithms Artificial intelligence Authoring Authorship Biology and Life Sciences Classification Computational linguistics Computer and Information Sciences Copyright Distribution moments Econometrics Engineering and Technology Humans Innovations Knowledge management Language Language processing Linguistics Mathematical models Modelling Natural language interfaces Natural language processing Networks Physical Sciences Physics Research and Analysis Methods Semantics Social Sciences Statistical analysis Text processing Texts Time series Transformation Variation Word sense disambiguation
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Summary:	Automatic identification of authorship in disputed documents has benefited from complex network theory as this approach does not require human expertise or detailed semantic knowledge. Networks modeling entire books can be used to discriminate texts from different sources and understand network growth mechanisms, but only a few studies have probed the suitability of networks in modeling small chunks of text to grasp stylistic features. 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. Since 73% of all series were stationary (ARIMA(p, 0, q)) and the remaining were integrable of first order (ARIMA(p, 1, q)), probability distributions could be obtained for the global network metrics. The metrics exhibit bell-shaped non-Gaussian distributions, and therefore distribution moments were used as learning attributes. With an optimized supervised learning procedure based on a nonlinear transformation performed by Isomap, 71 out of 80 texts were correctly classified using the K-nearest neighbors algorithm, i.e. a remarkable 88.75% author matching success rate was achieved. Hence, purely dynamic fluctuations in network metrics can characterize authorship, thus paving the way for a robust description of large texts in terms of small evolving networks.
ISSN:	1932-6203 1932-6203