A survey of modern authorship attribution methods
Abstract
Authorship attribution supported by statistical or computational methods has a long history starting from the 19th century and is marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed “Federalist Papers.” During the last decade, this scientific field has been developed substantially, taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e‐mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology, provided it is able to handle short and noisy text from multiple candidate authors. In this article, a survey of recent advances of the automated approaches to attributing authorship is presented, examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than on linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.
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STYLE methods are meant to analyze the writing style and suspect plagiarism within a document, in the absence of using a source collection for comparison.
“STYLE [22], [23], [32]-[35] methods are meant to analyze the writing style and suspect plagiarism within a document, in the absence of using a source collection for comparison.”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [22]
SEM and FUZZY methods incorporate different semantic features, thesaurus dictionaries, and lexical databases, such as WordNet.
“On the other hand, SEM [14], [15] and FUZZY [16]-[19] methods incorporate different semantic features, thesaurus dictionaries, and lexical databases, such as WordNet.”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [15]
In spite of its complexity, this method was found to be ideal in detecting reworded sentences, which greatly improves the precision of the results.
“In spite of its complexity, this method was found to be ideal in detecting reworded sentences, which greatly improves the precision of the results.”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [15]
Authorship attribution is inherently multidisciplinary, encompassing elements of linguistics, computer science, forensic science, and psychology.
“Authorship attribution is inherently multidisciplinary, encompassing elements of linguistics, computer science, forensic science, and psychology [Stamatatos, 2009].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Stamatatos, 2009
Cites
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“Previous studies on authorship attribution have proposed taxonomies of features to quantify the writing style, the so-called style markers, under different labels and criteria (Holmes, 1994; Stamatatos, Fakotakis, & Kokkinakis, 2000; Zheng et al., 2006).”
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“Authorship attribution is inherently multidisciplinary, encompassing elements of linguistics, computer science, forensic science, and psychology (Stamatatos, 2009).”
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- Authors
- Stamatatos, E.
- Affiliations
- University of the Aegean (GR)
- Corresponding author
- Stamatatos, Efstathios
- Venue
- Journal of the American Society for Information Science and Technology
- Publisher
- Wiley
- Published
- 2008-12-16
- Volume
- 60
- Issue
- 3
- Pages
- 538-556
- ISSN
- 1532-2882 (ISSN-L)
- 1532-2890
- OpenAlex
- W4250089123
- Cited by
- 1,176
- References
- 3
- Citations by year
- 2026: 60 · 2025: 60 · 2024: 50 · 2023: 63 · 2022: 84 · 2021: 78 · 2020: 88 · 2019: 130 · 2018: 94 · 2017: 109 · 2016: 90 · 2015: 74 · 2014: 70 · 2013: 54 · 2012: 34
- FWCI
- 20.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Society for Information Science and Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- authorship attribution, text classification, text representation, evaluation criteria, digital texts, computational requirements, machine learning, information retrieval, natural language processing
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