Cited by 23Authorship Attribution and ProfilingText and Document Classification TechnologiesNatural Language Processing Techniques

Stylometric Authorship Attribution of Collaborative Documents

Dauber, Edwin, Overdorf, Rebekah, Greenstadt, Rachel·2017·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-319-60080-2_9

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21 of 21 references verified0 citation errors · 0 not checkable

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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
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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