Authorship Attribution with Topic Models
Open accessAbstract
Authorship attribution deals with identifying the authors of anonymous texts. Traditionally, research in this field has focused on formal texts, such as essays and novels, but recently more attention has been given to texts generated by on-line users, such as e-mails and blogs. Authorship attribution of such on-line texts is a more challenging task than traditional authorship attribution, because such texts tend to be short, and the number of candidate authors is often larger than in traditional settings. We address this challenge by using topic models to obtain author representations. In addition to exploring novel ways of applying two popular topic models to this task, we test our new model that projects authors and documents to two disjoint topic spaces. Utilizing our model in authorship attribution yields state-of-the-art performance on several data sets, containing either formal texts written by a few authors or informal texts generated by tens to thousands of on-line users. We also present experimental results that demonstrate the applicability of topical author representations to two other problems: inferring the sentiment polarity of texts, and predicting the ratings that users would give to items such as movies.
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The challenge in social media stems from the brief nature of posts and a large pool of potential authors, making the attribution of short documents particularly difficult.
“The challenge in social media stems from the brief nature of posts and a large pool of potential authors, making the attribution of short documents particularly difficult [Aborisade and Anwar, 2018;Seroussi et al., 2014;Theophilo et al., 2021].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Seroussi et al., 2014
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Learning Stylometric Representations for Authorship Analysis
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Authorship Attribution using Filtered N-grams as Features
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Integrating Bidirectional Long Short-Term Memory with Subword Embedding for Authorship Attribution
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Contrastive Disentanglement for Authorship Attribution
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- Authors
- Seroussi, Y., Zukerman, I., & Bohnert, F.
- Affiliations
- Monash University (AU)
- Monash University
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2014-06
- Volume
- 40
- Issue
- 2
- Pages
- 269-310
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W2103577799
- Cited by
- 89
- References
- 68
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 11 · 2022: 7 · 2021: 7 · 2020: 9 · 2019: 10 · 2018: 14 · 2017: 9 · 2016: 7 · 2015: 2 · 2014: 1 · 2013: 1
- FWCI
- 4.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computational Linguistics (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Australian Research Council (LP0883416)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- authorship attribution, topic modeling, online texts, author representation, rating prediction, short text, blog posts, email
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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