Computational methods in authorship attribution

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On the features of translationese (2015, methodology): “In particular, we apply text classification, a methodology that has been used for classifying texts according to topic, genre, etc. (Sebastiani, 2002), but also for authorship attribution, classification of texts according to their authors' gender, age, provenance, and more (Koppel et al., 2009).”
On the Feasibility of Internet-Scale Author Identification (2012, background): “again study authorship recognition on a 10,000-author blog corpus [30].”
(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas (2019, background): “A series of works by (Koppel et al., 2011, 2009; Arg-amon et al., 2009; Koppel and Winter, 2014) and their shared tasks (Stamatatos et al., 2018) show huge progress on author proﬁling and attribute classiﬁcation tasks.”
Narrative Fingerprints: Multi-Scale Author Identification via Novelty Curve Dynamics (2026, unknown): “Comprehensive surveys (Stamatatos, 2009; Koppel et al., 2009) document the subsequent explosion of methods: character n-grams, part-of-speech trigrams, syntactic parse features, and deep learning approaches.”