A framework for authorship identification of online messages: Writing-style features and classification techniques

By Rong Zheng, Jiexun Li, Hsinchun Chen, Zan Huang. J. Assoc. Inf. Sci. Technol., 2006

734 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/31c7004afa43c587302ad5c7d0c9cab2f6485483

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Citations in the corpus

Computational methods in authorship attribution (2009, neutral): “A number of earlier works that have surveyed and/or compared various types of feature sets include Forsyth & Holmes (1996), Holmes (1998), McEnery & Oakes (2000), Love (2002), Zheng et al. (2006), Abbasi & Chen (2008) and Juola (2008).”
Authorship Attribution for Neural Text Generation (2020, background): “Structural and content-specific features were the best to assign authorship (Zheng et al., 2006).”
Detecting Offensive Language in Social Media to Protect Adolescent Online Safety (2012, background): “Based on the above observations, three types of features are developed to identify the level of offensiveness, which leveraged from authorship analysis research on cybercrime investigation [20-25]: style features, structural features, and content-specific features.”
Comparative study on methods of detecting research fronts using different types of citation (2009, methodology): “(de Vel, et al., 2001; Teng, Lai, Ma, & Li, 2004; Zheng, et al., 2006; Li, Zheng, & Chen, 2006) Moreover, provided the texts in question are in HTML form, measures related to HTML tag distribution (de Vel, et al., 2001), font color counts, and font size counts (Abbasi & Chen, 2005) can also be def”
A survey of modern authorship attribution methods (2009, background): “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).”
Connecting users across social media sites: a behavioral-modeling approach (2013, methodology): “In particular, machine learning techniques have been employed to detect authors in online messages [20] and in E-mails [8].”
Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace (2008, methodology): “In lieu of these textual traces, researchers have begun to use online stylometric analysis techniques as a forensic identi.cation tool, with recent application to email [De Vel et al. 2001], forums [Zheng et al. 2006], and program code [Gray et al. 1997].”
Detecting Hoaxes, Frauds, and Deception in Writing Style Online (2012, background): “proposed the Writeprints features that can represent an author’s writing style in relatively short documents, especially in online messages [30].”