Authorship Attribution

By Patrick Juola. Foundations and Trends in Information Retrieval, 2008

1,062 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f87d363f7133461b5d7524693a0692408ac99517

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Citations in the corpus

Information Retrieval for E-Discovery (2013, background): “, based on patterns in syntactic or lexical choice and/or systematic variations in spelling or layout) [76].”
Manipulation of online reviews: An analysis of ratings, readability, and sentiments (2012, background): “As writing style varies with the background of an individual, intuitively, reviews written by different consumers will be random in the case where there is no manipulation [21,24].”
Dynamic Wordclouds and Vennclouds for Exploratory Data Analysis (2014, background): “Sepa-rately, author identiﬁcation (or idiolect analysis) attempts attribution of documents (e.g., Shake-speare’s plays or the Federalist papers) by comparing the author’s writing style, focusing on stylistic and content less words – for a review see (Juola, 2006).”
Identifying translationese at the word and sub-word level (2016, background): “…for automatic author attribution, i.e., " inferring characteristics of the author from the characteristics of documents written by that author " (Juola, 2006, p. 233), for example, for identifying authors of newspaper articles (Diederich, Kindermann, Leopold, & Paass, 2003), or for determining…”
A survey of modern authorship attribution methods (2009, background): “Previous studies on authorship attribution 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).”
Function Words for Chinese Authorship Attribution (2012, background): “Function words are an important feature set for Authorship Attribution (hereafter “AA”) because they are considered topic-independent or contextfree, and that they are largely used in an unconscious manner (Holmes, 1994; Stamatatos, 2009; Koppel et al., 2009).”
Applications of Topic Models (2017, neutral): “the question of authorship attribution [Juola, 2006].”
On the Feasibility of Internet-Scale Author Identification (2012, background): “We point the reader to Juola’s excellent survey [35].”
Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs (2011, background): “, non-topical) elements like articles and prepositions to characterize the utterer in some way has a long history, including in authorship attribution (Mosteller and Wallace, 1984; Juola, 2008), personality-type classification (Argamon et al.”
Adversarial stylometry: Circumventing authorship recognition to preserve privacy and anonymity (2012, background): “It has since been modified with better accuracy that has been supported by researchers but questions of accuracy are still considered by some to be the biggest problem faced by the field [Juola 2006].”
Syntactic N-grams as machine learning features for natural language processing (2014, background): “Authorship attribution deals with an old and difficult question: how to assign a text of unknown or disputed authorship to a member of a set of candidate authors for whom undisputed samples of texts are available [10].”
Detecting Hoaxes, Frauds, and Deception in Writing Style Online (2012, background): “Despite our ability to communicate, language is learned on an individual basis resulting in an individual writing style [11].”
Complexity of Word Collocation Networks: A Preliminary Structural Analysis (2013, methodology): “For details on authorship attribution, please see the surveys by Juola (2006), Koppel et al.”