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A survey of modern authorship attribution methods

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  • Efstathios Stamatatos

Abstract

Authorship attribution supported by statistical or computational methods has a long history starting from the 19th century and is marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed “Federalist Papers.” During the last decade, this scientific field has been developed substantially, taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e‐mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology, provided it is able to handle short and noisy text from multiple candidate authors. In this article, a survey of recent advances of the automated approaches to attributing authorship is presented, examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than on linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.

Suggested Citation

  • Efstathios Stamatatos, 2009. "A survey of modern authorship attribution methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 538-556, March.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:3:p:538-556
    DOI: 10.1002/asi.21001
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    2. de Arruda, Henrique F. & Marinho, Vanessa Q. & Lima, Thales S. & Amancio, Diego R. & Costa, Luciano da F., 2018. "An image analysis approach to text analytics based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 110-120.
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    4. Jacques Savoy & Olena Zubaryeva, 2012. "Simple and efficient classification scheme based on specific vocabulary," Computational Management Science, Springer, vol. 9(3), pages 401-415, August.
    5. Sanda-Maria Avram & Mihai Oltean, 2022. "A Comparison of Several AI Techniques for Authorship Attribution on Romanian Texts," Mathematics, MDPI, vol. 10(23), pages 1-35, December.
    6. H Andrew Schwartz & Johannes C Eichstaedt & Margaret L Kern & Lukasz Dziurzynski & Stephanie M Ramones & Megha Agrawal & Achal Shah & Michal Kosinski & David Stillwell & Martin E P Seligman & Lyle H U, 2013. "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
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    9. Matthew J. Schneider & Shawn Mankad, 2021. "A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(3), pages 66-83, September.
    10. Maryam Ebrahimpour & Tālis J Putniņš & Matthew J Berryman & Andrew Allison & Brian W-H Ng & Derek Abbott, 2013. "Automated Authorship Attribution Using Advanced Signal Classification Techniques," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
    11. Kargin, Vladislav, 2016. "On variation of word frequencies in Russian literary texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 328-334.
    12. Ballandonne, Matthieu & Cersosimo, Igor, 2022. "Towards a “Text as Data” Approach in the History of Economics: An Application to Adam Smith’s Classics," OSF Preprints mg3zb, Center for Open Science.
    13. Catalin Stoean & Daniel Lichtblau, 2020. "Author Identification Using Chaos Game Representation and Deep Learning," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    14. Matilde Trevisani & Arjuna Tuzzi, 2015. "A portrait of JASA: the History of Statistics through analysis of keyword counts in an early scientific journal," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1287-1304, May.
    15. Oleg Sobchuk & Artjoms Šeļa, 2024. "Computational thematics: comparing algorithms for clustering the genres of literary fiction," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    16. Andi Rexha & Mark Kröll & Hermann Ziak & Roman Kern, 2018. "Authorship identification of documents with high content similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 223-237, April.
    17. Haoran Zhu & Lei Lei, 2022. "The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis," SAGE Open, , vol. 12(2), pages 21582440221, April.
    18. Daniel Embarcadero-Ruiz & Helena Gómez-Adorno & Alberto Embarcadero-Ruiz & Gerardo Sierra, 2022. "Graph-Based Siamese Network for Authorship Verification," Mathematics, MDPI, vol. 10(2), pages 1-24, January.
    19. Ullah, Farhan & Jabbar, Sohail & Al-Turjman, Fadi, 2020. "Programmers' de-anonymization using a hybrid approach of abstract syntax tree and deep learning," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    20. 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.
    21. Silvia Corbara & Alejandro Moreo & Fabrizio Sebastiani, 2023. "Syllabic quantity patterns as rhythmic features for Latin authorship attribution," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 128-141, January.
    22. Malik Muhammad Saad Missen & Sajeeha Qureshi & Nadeem Salamat & Nadeem Akhtar & Hina Asmat & Mickaël Coustaty & V. B. Surya Prasath, 2020. "Scientometric analysis of social science and science disciplines in a developing nation: a case study of Pakistan in the last decade," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 113-142, April.
    23. Nils-Axel M?rner, 2018. "Evaluation of the Performance and Efficiency of the Automated Linguistic Features for Author Identification in Short Text Messages Using Different Variable Selection Techniques," Studies in Media and Communication, Redfame publishing, vol. 6(2), pages 83-102, December.
    24. Ahmed Shamsul Arefin & Renato Vimieiro & Carlos Riveros & Hugh Craig & Pablo Moscato, 2014. "An Information Theoretic Clustering Approach for Unveiling Authorship Affinities in Shakespearean Era Plays and Poems," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-12, October.

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