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Authorship identification of documents with high content similarity

Author

Listed:
  • Andi Rexha

    (Know-Center GmbH)

  • Mark Kröll

    (Know-Center GmbH)

  • Hermann Ziak

    (Know-Center GmbH)

  • Roman Kern

    (Know-Center GmbH)

Abstract

The goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. Unlike the majority of the work done in this field (i.e. authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors. Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper. Both studies confirmed that this task is quite challenging. To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based. The findings of our detailed analysis could (1) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (2) assist forensic experts or linguists to create profiles of writers, (3) support intelligence applications to analyze aggressive and threatening messages and (4) help editor conformity by adhering to, for instance, journal specific writing style.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:115:y:2018:i:1:d:10.1007_s11192-018-2661-6
    DOI: 10.1007/s11192-018-2661-6
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Tingting Zhang & Baozhen Lee & Qinghua Zhu, 2019. "Semantic measure of plagiarism using a hierarchical graph model," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 209-239, October.
    2. Mark Bukowski & Sandra Geisler & Thomas Schmitz-Rode & Robert Farkas, 2020. "Feasibility of activity-based expert profiling using text mining of scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 579-620, May.
    3. Guillaume Cabanac & Ingo Frommholz & Philipp Mayr, 2018. "Bibliometric-enhanced information retrieval: preface," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1225-1227, August.
    4. Ankita Dhar & Himadri Mukherjee & Shibaprasad Sen & Md Obaidullah Sk & Amitabha Biswas & Teresa Gonçalves & Kaushik Roy, 2022. "Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents," Future Internet, MDPI, vol. 14(10), pages 1-20, September.

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