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On the role of words in the network structure of texts: Application to authorship attribution

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  • Akimushkin, Camilo
  • Amancio, Diego R.
  • Oliveira, Osvaldo N.

Abstract

Well-established automatic analyses of texts mainly consider frequencies of linguistic units, e.g. letters, words, and bigrams. In a recent, alternative approach, medium and large-scale text structures were used in opposition to the belief that text structure is dominated by the language features. In this paper, we introduce a generalized similarity measure to compare texts which accounts for both the network structure of texts and the role of individual words in the networks. The similarity measure is used for authorship attribution of three collections of books, each composed of 8 authors and 10 books per author. High accuracy rates were obtained with typical values between 90% and 98.75%, much higher than with the traditional term frequency-inverse document frequency (tf-idf) approach for the same collections. These accuracies are also higher than those obtained solely with the topology of networks. We conclude that the different properties of specific words on the macroscopic scale structure of a whole text are as relevant as their frequency of appearance; conversely, considering the identity of nodes brings further knowledge about a piece of text represented as a network.

Suggested Citation

  • Akimushkin, Camilo & Amancio, Diego R. & Oliveira, Osvaldo N., 2018. "On the role of words in the network structure of texts: Application to authorship attribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 49-58.
    • Handle: RePEc:eee:phsmap:v:495:y:2018:i:c:p:49-58
    DOI: 10.1016/j.physa.2017.12.054
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    References listed on IDEAS

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    1. D. R. Amancio & M. G. V. Nunes & O. N. Oliveira & L. F. Costa, 2012. "Using complex networks concepts to assess approaches for citations in scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 827-842, June.
    2. Amancio, Diego R. & Oliveira Jr., Osvaldo N. & Costa, Luciano da F., 2012. "Structure–semantics interplay in complex networks and its effects on the predictability of similarity in texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4406-4419.
    3. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    4. Liang, Wei, 2017. "Spectra of English evolving word co-occurrence networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 802-808.
    5. Amancio, Diego Raphael & Oliveira, Osvaldo Novais & da Fontoura Costa, Luciano, 2012. "Three-feature model to reproduce the topology of citation networks and the effects from authors’ visibility on their h-index," Journal of Informetrics, Elsevier, vol. 6(3), pages 427-434.
    6. Borut Lužar & Zoran Levnajić & Janez Povh & Matjaž Perc, 2014. "Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-5, April.
    7. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    8. Mehri, Ali & Darooneh, Amir H. & Shariati, Ashrafalsadat, 2012. "The complex networks approach for authorship attribution of books," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2429-2437.
    9. Viana, Matheus P. & Amancio, Diego R. & da F. Costa, Luciano, 2013. "On time-varying collaboration networks," Journal of Informetrics, Elsevier, vol. 7(2), pages 371-378.
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    Cited by:

    1. Christian-Daniel Curiac & Alex Doboli & Daniel-Ioan Curiac, 2022. "Co-Occurrence-Based Double Thresholding Method for Research Topic Identification," Mathematics, MDPI, vol. 10(17), pages 1-10, August.
    2. Mihailo Škorić & Ranka Stanković & Milica Ikonić Nešić & Joanna Byszuk & Maciej Eder, 2022. "Parallel Stylometric Document Embeddings with Deep Learning Based Language Models in Literary Authorship Attribution," Mathematics, MDPI, vol. 10(5), pages 1-27, March.

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