A survey of modern authorship attribution methods

Abstract

Authorship attribution supported by statistical or computational methods has a long history starting from 19th century and marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed Federalist Papers. During the last decade, this scientific field has been developed substantially taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology provided it is able to handle short and noisy text from multiple candidate authors. In this paper, a survey of recent advances of the automated approaches to attributing authorship is presented examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.

Corpus of transcribed parliamentary speeches for authorship attribution and author profiling tasks

“…Pasiūlyta nauja paradigma -automatiniai prižiūrimo mašininio mokymo metodai (Kotsiantis 2007), skirti tekstų klasifikavimui (Sebastiani 2002), jie pradėti taikyti autorystės nustatymo bei autoriaus profilio sudarymo tyrimams (Stamatatos 2009). Šių metodų populiarumą lėmė tai, jog jie efektyviai veikia turint dideles autorių imtis, kai žmogui-ekspertui tiesiog per sunku surasti skirtumus tarp tiek daug grupių ir sukonstruoti kiekvieną iš jų apibūdinančius taisyklių šablonus.…”

Authorship attribution based on a probabilistic topic model

“…Authorship attribution owns a long-standing history (Mosteller & Wallace, 1964, Juola, 2006, Zheng, Li, Chen, & Huang, 2006, Stamatatos, 2009. As a first solution, past studies have proposed using a unitary invariant measure that must reflect the style of a given author but should vary from one writer to another.…”

Syntactic Dependency-Based N-grams: More Evidence of Usefulness in Classification

“…We discussed the state of the art of the authorship attribution problem in our previous work on application of syntactic n-grams for authorship attribution [1]; also see various related works on authorship attribution [7,8,9,10], among many others. Here we will just briefly state the problem.…”