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Quantification and identification of authorial writing style through higher-order text network modeling and analysis

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  • Deng, Hongzhong
  • Wu, Chengxing
  • Ge, Bingfeng
  • Wu, Hongqian

Abstract

Determining the true author of anonymized texts has important applications ranging from text classification and information extraction to forensic investigations. Despite substantial progress, current authorship identification solutions are limited to extracting straightforward semantic relationships in writing styles, lacking consideration for higher-order features among multiple vocabulary, phrases, or sentences in language structure. Here, we propose a novel approach based on hypernetwork theory to encode higher-order text features into a unified text hyper-network and investigate whether the hyper-order topological features of the text hyper-network contribute to revealing the author's stylistic preferences. Our results indicate that metrics of the text hyper-network, such as hyperdegree, average shortest path length, and intermittency, can capture more information about the author's writing styles. More importantly, in the author identification task of 170 novels, our method accurately distinguished the authorship of 81% of the novels, surpassing the accuracy of the method of using paired word relationships. This further highlights the importance of higher-order features in text analysis, beyond mere pairwise interactions of words.

Suggested Citation

  • Deng, Hongzhong & Wu, Chengxing & Ge, Bingfeng & Wu, Hongqian, 2025. "Quantification and identification of authorial writing style through higher-order text network modeling and analysis," Journal of Informetrics, Elsevier, vol. 19(1).
  • Handle: RePEc:eee:infome:v:19:y:2025:i:1:s1751157724001159
    DOI: 10.1016/j.joi.2024.101603
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    References listed on IDEAS

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    1. Chao Min & Qingyu Chen & Erjia Yan & Yi Bu & Jianjun Sun, 2021. "Citation cascade and the evolution of topic relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 110-127, January.
    2. S. Lozano & L. Calzada-Infante & B. Adenso-Díaz & S. García, 2019. "Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 609-629, August.
    3. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.
    4. Jacques Savoy, 2016. "Estimating the probability of an authorship attribution," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(6), pages 1462-1472, June.
    5. Chekhovich, Yury V. & Khazov, Andrey V., 2022. "Analysis of duplicated publications in Russian journals," Journal of Informetrics, Elsevier, vol. 16(1).
    6. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    7. Yu-Wei Chang & Mu-Hsuan Huang & Chiao-Wen Lin, 2015. "Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2071-2087, December.
    8. Shlomo Argamon & Casey Whitelaw & Paul Chase & Sobhan Raj Hota & Navendu Garg & Shlomo Levitan, 2007. "Stylistic text classification using functional lexical features," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(6), pages 802-822, April.
    9. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    10. 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.
    11. Chen, Guo & Hong, Siqi & Du, Chenxin & Wang, Panting & Yang, Zeyu & Xiao, Lu, 2024. "Comparing semantic representation methods for keyword analysis in bibliometric research," Journal of Informetrics, Elsevier, vol. 18(3).
    12. Diego Raphael Amancio, 2015. "Comparing the topological properties of real and artificially generated scientific manuscripts," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1763-1779, December.
    13. Lu, Chao & Bu, Yi & Dong, Xianlei & Wang, Jie & Ding, Ying & Larivière, Vincent & Sugimoto, Cassidy R. & Paul, Logan & Zhang, Chengzhi, 2019. "Analyzing linguistic complexity and scientific impact," Journal of Informetrics, Elsevier, vol. 13(3), pages 817-829.
    14. 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.
    15. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
    16. Wu, Chengxing & Duan, Dongli & Xiao, Renbin, 2023. "A novel dimension reduction method with information entropy to evaluate network resilience," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 620(C).
    17. D. I. Holmes, 1992. "A Stylometric Analysis of Mormon Scripture and Related Texts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 155(1), pages 91-120, January.
    18. Moshe Koppel & Yaron Winter, 2014. "Determining if two documents are written by the same author," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(1), pages 178-187, January.
    19. Garg, Muskan & Kumar, Mukesh, 2018. "The structure of word co-occurrence network for microblogs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 698-720.
    20. Rong Zheng & Jiexun Li & Hsinchun Chen & Zan Huang, 2006. "A framework for authorship identification of online messages: Writing‐style features and classification techniques," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 378-393, February.
    21. Chao Lu & Yi Bu & Jie Wang & Ying Ding & Vetle Torvik & Matthew Schnaars & Chengzhi Zhang, 2019. "Examining scientific writing styles from the perspective of linguistic complexity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(5), pages 462-475, May.
    22. Corrêa, Edilson A. & Amancio, Diego R., 2019. "Word sense induction using word embeddings and community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 180-190.
    23. 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.
    24. Unai Alvarez-Rodriguez & Federico Battiston & Guilherme Ferraz Arruda & Yamir Moreno & Matjaž Perc & Vito Latora, 2021. "Evolutionary dynamics of higher-order interactions in social networks," Nature Human Behaviour, Nature, vol. 5(5), pages 586-595, May.
    25. Moshe Koppel & Jonathan Schler & Shlomo Argamon, 2009. "Computational methods in authorship attribution," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 9-26, January.
    26. Khreisat, Laila, 2009. "A machine learning approach for Arabic text classification using N-gram frequency statistics," Journal of Informetrics, Elsevier, vol. 3(1), pages 72-77.
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