Cited by 90Authorship Attribution and ProfilingText and Document Classification TechnologiesNatural Language Processing Techniques

Authorship attribution with thousands of candidate authors

Koppel, Moshe, Schler, Jonathan, Argamon, Shlomo, Messeri, Eran·2006·Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

Abstract

In this paper, we use a blog corpus to demonstrate that we can often identify the author of an anonymous text even where there are many thousands of candidate authors. Our approach combines standard information retrieval methods with a text categorization meta-learning scheme that determines when to even venture a guess.

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Crossref · 10.1145/1148170.1148304

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6 of 6 references verified0 citation errors · 0 not checkable

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  • Factors influencing the performance of existing authorship attribution models include the size of the training text, the number of candidate authors, and the imbalanced distribution of training texts among candidate authors.

    “Factors influencing the performance of existing authorship attribution models include the size of the training text [Hirst and Feiguina, 2007;Marton et al., 2005], the number of candidate authors [Koppel et al., 2006], and the imbalanced distribution of training texts among candidate authors [Stamatatos, 2008].”

    From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Koppel et al., 2006

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Authors
Koppel, M., Schler, J., Argamon, S., & Messeri, E.
Affiliations
  • Bar-Ilan University (IL)
  • Illinois Institute of Technology (US)
Venue
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Publisher
ACM
Published
2006-08-06
Pages
659-660
OpenAlex
W2073556086
Cited by
90
References
6
Citations by year
2026: 1 · 2023: 1 · 2022: 3 · 2021: 7 · 2020: 5 · 2019: 3 · 2018: 3 · 2017: 2 · 2016: 7 · 2015: 9 · 2014: 8 · 2013: 5 · 2012: 11
FWCI
3.12
Type
conference paper
Language
en
Open access
closed
Locations
  • International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
  • Touro Scholar (Touro College) (submittedVersion)
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
authorship attribution, anonymous texts, blog corpus, text classification, meta-learning, information retrieval

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