Authorship Analysis Studies: A Survey
TLDR
Focus is on outlining the Stylometric features that allow distinguishing between authors and on listing the diverse techniques used to classify an author's texts.Abstract:
objective in this paper is to provide a review of the different studies done on authorship analysis. Focus is on outlining the Stylometric features that allow distinguishing between authors and on listing the diverse techniques used to classify an author's texts.read more
Citations
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Journal ArticleDOI
The Statistical Study of Literary Vocabulary
TL;DR: The Statistical Study of Literary Vocabulary by G. Udny Yule is a statistical event which, though of great rarity, is all the more welcome when it occurs as mentioned in this paper.
Journal ArticleDOI
Surveying Stylometry Techniques and Applications
TL;DR: An extensive performance analysis is performed on a corpus of 1,000 authors to investigate authorship attribution, verification, and clustering using 14 algorithms from the literature.
Journal ArticleDOI
Authorship verification applied to detection of compromised accounts on online social networks
TL;DR: A pure text mining approach to check if an account has been compromised based on its posts content and shows that the developed method is stable and can detect the compromised accounts.
References
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Journal IssueDOI
A survey of modern authorship attribution methods
TL;DR: A survey of recent advances of the automated approaches to attributing authorship is presented, examining their characteristics for both text representation and text classification.
Book
Inference and disputed authorship : The Federalist
TL;DR: Inference and Disputed Authorship of the Federalist Papers as discussed by the authors is a classic work that applies mathematics, including the once-controversial Bayesian analysis, to the heart of a literary and historical problem by studying frequently used words in the texts.
Journal ArticleDOI
Automatically Categorizing Written Texts by Author Gender
TL;DR: It is shown that automated text categorization techniques can exploit combinations of simple lexical and syntactic features to infer the gender of the author of an unseen formal written document with approximately 80 per cent accuracy.
Journal IssueDOI
A framework for authorship identification of online messages: Writing-style features and classification techniques
TL;DR: A framework for authorship identification of online messages to address the identity-tracing problem is developed and four types of writing-style features are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship ofonline messages.