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Effects of Age and Gender on Blogging
- Jonathan Schler, Moshe Koppel, S. Argamon, J. Pennebaker
- ArtAAAI Spring Symposium: Computational Approaches…
- 2006
Analysis of a corpus of tens of thousands of blogs – incorporating close to 300 million words – indicates significant differences in writing style and content between male and female bloggers as well…
Automatically Categorizing Written Texts by Author Gender
- Moshe Koppel, S. Argamon, Anat Rachel Shimoni
- Computer ScienceLiterary and Linguistic Computing
- 1 November 2002
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.
Gender, genre, and writing style in formal written texts
- S. Argamon, Moshe Koppel, J. Fine, Anat Rachel Shimoni
- Linguistics
- 21 January 2003
This article explores differences between male and female writing in a large subset of the British National Corpus covering a range of genres. Several classes of simple lexical and syntactic features…
Computational methods in authorship attribution
- Moshe Koppel, Jonathan Schler, S. Argamon
- Computer ScienceJ. Assoc. Inf. Sci. Technol.
- 2009
Three scenarios are considered here for which solutions to the basic attribution problem are inadequate; it is shown how machine learning methods can be adapted to handle the special challenges of that variant.
Automatically profiling the author of an anonymous text
- S. Argamon, Moshe Koppel, J. Pennebaker, Jonathan Schler
- Computer ScienceCACM
- 1 February 2009
ImagIne that you have been gIven an Important text of unknown authorship, and wish to know as much as possible about the unknown author (demographics, personality, cultural background, among others),…
Authorship verification as a one-class classification problem
- Moshe Koppel, Jonathan Schler
- MathematicsInternational Conference on Machine Learning
- 4 July 2004
A new learning-based method for adducing the "depth of difference" between two example sets is presented and evidence that this method solves the authorship verification problem with very high accuracy is offered.
Authorship attribution in the wild
- Moshe Koppel, Jonathan Schler, S. Argamon
- Computer ScienceLanguage Resources and Evaluation
- 13 January 2010
This paper shows the precise relationship between attribution precision and four parameters: the size of the candidate set, the quantity of known-text by the candidates, the length of the anonymous text and a certain robustness score associated with a attribution.
Measuring Differentiability: Unmasking Pseudonymous Authors
- Moshe Koppel, Jonathan Schler, Elisheva Bonchek-Dokow
- MathematicsJournal of machine learning research
- 1 December 2007
A new learning-based method for adducing the "depth of difference" between two example sets is presented and evidence that this method solves the authorship verification problem with very high accuracy is offered.
Determining an author's native language by mining a text for errors
- Moshe Koppel, Jonathan Schler, Kfir Zigdon
- Computer ScienceKnowledge Discovery and Data Mining
- 21 August 2005
It is shown that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy and serve as features for support vector machines that learn to classify texts according to author native language.
Determining if two documents are written by the same author
- Moshe Koppel, Yaron Winter
- MathematicsJ. Assoc. Inf. Sci. Technol.
- 1 January 2014
This article offers an (almost) unsupervised method for solving the authorship attribution problem by using repeated feature subsampling methods to determine if one document of the pair allows us to select the other from among a background set of “impostors” in a sufficiently robust manner.
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