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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Authorship attribution based on Life-Like Network Automata

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Author(s):
Machicao, Jeaneth [1] ; Correa, Jr., Edilson A. [2] ; Miranda, Gisele H. B. [2] ; Amancio, Diego R. [2] ; Bruno, Odemir M. [1]
Total Authors: 5
Affiliation:
[1] Univ Sao Paulo, Sao Carlos Inst Phys, Sci Comp Grp, POB 369, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PLoS One; v. 13, n. 3 MAR 22 2018.
Web of Science Citations: 0
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 structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks. (AU)

FAPESP's process: 14/20830-0 - Using complex networks to recognize patterns in written texts
Grantee:Diego Raphael Amancio
Support type: Regular Research Grants
FAPESP's process: 15/05899-7 - Pattern recognition in complex networks through automata
Grantee:Gisele Helena Barboni Miranda
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 17/13464-6 - Modelling citation and information graphs: a complex network approach
Grantee:Diego Raphael Amancio
Support type: Scholarships abroad - Research
FAPESP's process: 16/19069-9 - Using semantical information to classify texts modelled as complex networks
Grantee:Diego Raphael Amancio
Support type: Regular Research Grants
FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
Grantee:Odemir Martinez Bruno
Support type: Regular Research Grants