Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace

By Ahmed Ali Abbasi, Hsinchun Chen. TOIS, 2008

523 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/c2aa3806c3bc190e0d4fa5a229a115552b343c41

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

Citations in the corpus

Evaluating sentiment in financial news articles (2012, unknown): “Techniques include measures of similarity between documents and focusing on word choice and syntax usage (Abbasi and Chen, 2008).”
Topic or Style? Exploring the Most Useful Features for Authorship Attribution (2018, methodology): “Following previous studies (Abbasi and Chen, 2008; Stamatatos, 2009), feature groups are divided into three types (see Table 3): • Style: Style-based features, such as usage of function words, digits and punctuation, capture an author’s writing style.”
Computational methods in authorship attribution (2009, unknown): “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).”
Adversarial stylometry: Circumventing authorship recognition to preserve privacy and anonymity (2012, methodology): “…Syntactic Function Words 403 frequency of function words POS tags 22 Frequency of Parts of speech tag Punctuation 8 Frequency and percentage of colon, semicolon, qmark, period, exclamation, comma The Writeprints-Static feature set, adopted from the Writeprints approach [Abbasi and Chen 2008].”
Attributing Fake Images to GANs: Analyzing Fingerprints in Generated Images (2018, background): “A range of techniques can be employed to attribute a text to a particular author Earlier techniques rely on hand-crafted and human designed features [4], while more recent techniques are built on deep neural features [7, 19, 7].”
Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge (2011, background): “This is a daunting task: a long line of research has demonstrated the feasibility of de-anonymization on various datasets [27], [21], [13], [17], [25], [26], [14], [1] and theoretical evidence exists for the impossibility of de-anonymization of high-dimensional data [3].”
Detecting Hoaxes, Frauds, and Deception in Writing Style Online (2012, background): “Other work looks into the impact of stylometry on pseudonymity [23] and author segmentation [1].”