Stylometric Authorship Attribution of Collaborative Documents
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Collaborative texts blend multiple writing styles, masking individual contributions and obscuring distinct authorial signals.
“Collaborative texts, which blend multiple writing styles, further mask individual contributions and obscure distinct authorial signals [Dauber et al., 2017].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Dauber et al., 2017
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- Authors
- Dauber, E., Overdorf, R., & Greenstadt, R.
- Affiliations
- Drexel University (US)
- Corresponding author
- Dauber, Edwin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 115-135
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2621283262
- Cited by
- 22
- References
- 21
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 6 · 2020: 6 · 2018: 2
- FWCI
- 1.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- stylometry, authorship attribution, multi-label classification, support vector machine, text segmentation, writing style features
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