Authorship attribution with thousands of candidate authors
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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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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On authorship attribution: document’s length may not matter
Bertini, F., Branchetti, S., Calarota, G., & Montesi, D. (2026) · it - Information Technology · doi:10.1515/itit-2024-0099
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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