A Scalable Framework for Stylometric Analysis of Multi-author Documents

  • Raheem Sarwar
  • Chenyun Yu
  • Sarana Nutanong
  • Norawit Urailertprasert
  • Nattapol Vannaboot
  • Thanawin Rakthanmanon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


Stylometry is a statistical technique used to analyze the variations in the author’s writing styles and is typically applied to authorship attribution problems. In this investigation, we apply stylometry to authorship identification of multi-author documents (AIMD) task. We propose an AIMD technique called Co-Authorship Graph (CAG) which can be used to collaboratively attribute different portions of documents to different authors belonging to the same community. Based on CAG, we propose a novel AIMD solution which (i) significantly outperforms the existing state-of-the-art solution; (ii) can effectively handle a larger number of co-authors; and (iii) is capable of handling the case when some of the listed co-authors have not contributed to the document as a writer. We conducted an extensive experimental study to compare the proposed solution and the best existing AIMD method using real and synthetic datasets. We show that the proposed solution significantly outperforms existing state-of-the-art method.


Stylometry Authorship identification Co-Authorship Graph Multi-author documents 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Raheem Sarwar
    • 1
  • Chenyun Yu
    • 1
  • Sarana Nutanong
    • 1
  • Norawit Urailertprasert
    • 2
  • Nattapol Vannaboot
    • 2
  • Thanawin Rakthanmanon
    • 2
    • 3
  1. 1.Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR, China
  2. 2.Department of Computer EngineeringKasetsart UniversityBangkokThailand
  3. 3.Vidyasirimedhi Institute of Science and TechnologyRayongThailand

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