A survey of modern authorship attribution methods

By Efstathios Stamatatos. 2009

944 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text (2013, methodology): “We will also provide an anonymized author id for each story, which could allow additional research such as using other works by the same author when understanding a story, or research on authorship at-tribution (e.g., Stamatatos, 2009).”
Story Cloze Task: UW NLP System (2017, methodology): “Character n -grams are useful features in the detection of author style (Stamatatos, 2009) or language identiﬁcation (Lui and Baldwin, 2011).”
Predicting Authorship and Author Traits from Keystroke Dynamics (2018, background): “Early work in authorship analysis has typically been concerned with ﬁnding the author of a text, i.e., authorship attribution (Mosteller and Wallace, 1964; Stamatatos, 2009).”
On the Feasibility of Internet-Scale Author Identification (2012, background): “5An example of a rewrite rule is A:PP→ P:PREP + PC:N, meaning that an adverbial prepositional phrase is constituted by a preposition followed by a noun phrase as a prepositional complement [21].”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, methodology): “Such work uses openvocabulary linguistic features in addition to a priori lexicon based features in predictive models for tasks such as stylistics/authorship attribution [47–49], emotion prediction [50,51], interaction or flirting detection [52,53], or sentiment analysis [54–57].”
Particle Swarm Model Selection for Authorship Verification (2009, background): “Most of the work on AV has focused on developing specific features (stylometric, lexical, character-level, syntactic, semantic) able to characterize the writing style of authors, thus putting emphasis on feature extraction and selection [7, 11, 10, 1], see [13] for a comprehensive review.”
Can Investors Detect Managers' Lack of Spontaneity? Adherence to Predetermined Scripts during Earnings Conference Calls (2016, neutral): “they reflect minor or even unconscious preferences of the speaker (e.g, Mosteller and Wallace 1963; Burrows 1987; Koppel, Schler, and Argamon 2009; Stamatatos 2009; Mosteller 2010).”
Syntactic N-grams as machine learning features for natural language processing (2014, methodology): “The automated approaches to this problem involve the use of statistical or machine learning methods (Stamatatos, 2009).”
Computational Sociolinguistics: A Survey (2016, background): “Another relevant field is computational stylometry (Daelemans 2013; Holmes 1998; Stamatatos 2009), which focuses on computational models of writing style for various tasks such as plagiarism detection, author profiling and authorship attribution.”
Complexity of Word Collocation Networks: A Preliminary Structural Analysis (2013, background): “Stevanak et al. (2010), for example, used word collocation networks to distinguish between novels and news articles, but they did not perform a distributional analysis of the different global network properties they used, thereby leaving open how good those properties truly were as features for genr”
Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012, methodology): “However, an evidence of plagiarism (i.e., providing the source document) is crucial to a human investigator, a disadvantage of STYLE methods.”
Topic or Style? Exploring the Most Useful Features for Authorship Attribution (2018, methodology): “Previous work on feature exploration for authorship at-tribution, focused on the overall effectiveness of features without considering the characteristics of the datasets to which they were applied, e.g. (Grieve, 2007; Guthrie, 2008; Stamatatos, 2009; Brennan et al., 2012; Sapkota et al., 2015).”