Results 261 to 270 of about 276,851 (299)

Attributing authorship of revisioned content

Proceedings of the 22nd international conference on World Wide Web, 2013
A considerable portion of web content, from wikis to collaboratively edited documents, to code posted online, is revisioned. We consider the problem of attributing authorship to such revisioned content, and we develop scalable attribution algorithms that can be applied to very large bodies of revisioned content, such as the English Wikipedia.
Luca de Alfaro, Michael Shavlovsky
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Feature selections for authorship attribution

Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013
The authorship attribution (AA) problem can be viewed as a categorization problem. To determine the most effective features to discriminate between different authors, we have evaluated six independent feature-scoring selection functions (information gain, pointwise mutual information, odds ratio, χ2, DIA, and the document frequency (df)).
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Authorship Attribution Using Entropy

Journal of Quantitative Linguistics, 2013
Abstract We propose a new methodology for testing the authorship of a relatively small work compared with the large body of an author’s cannon. Our approach is based on comparing the entropy of the two samples. The difficulty lies in the fact that known estimators of entropy tend to have a large bias even when the sample size is fairly large.
Michael Grabchak, Zhiyi Zhang 0003, D. T. Zhang   +2 more
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Attributing Authorship

2002
Recent literary scholarship has seen a shift of interest away from questions of attribution. Yet these questions remain urgent and important for any historical study of writing, and have been given a powerful new impetus by advances in statistical studies of language and the coming on line of large databases of texts in machine-searchable form.
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Computational methods in authorship attribution

Journal of the American Society for Information Science and Technology, 2008
AbstractStatistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each.
Moshe Koppel, Jonathan Schler, Shlomo Argamon   +2 more
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Authorship attribution

Authorship attribution deals with identifying the authors of anonymous texts. This project considers the problem of authorship attribution for two types of text: online reviews and court judgments.
Zukerman, Ingrid, Seroussi, Yanir, Smythe, Russell, Partovi, Andisheh, Valente, Joseph   +4 more
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Statistical Models for Authorship Attribution

2019
A combination of three hypothesis methods has been proposed for authorship attribution. These are: the Student’s t-test, the Kolmogorov-Smirnov’s test and the chi-square test. This combination of methods can perfect adequacy of authorship attribution. With the help of these methods, the quantity of phoneme groups is minimized.
Iryna Khomytska, Vasyl Teslyuk
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Authorship Attribution of Arabic Articles

2019
With the huge size and large diversity of web content and the appearance of more social media platforms and blog websites, more people are contributing content of varying quality. Many users prefer to keep themselves anonymous when posting material to the web, which resulted in more pieces of text: articles, blogs, essays and emails being published ...
Maha Hajja, Ahmad Yahya, Adnan H. Yahya
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On sequential selection of attributes to be discretized for authorship attribution

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017
Different data mining techniques are employed in stylometry domain for performing authorship attribution tasks. Sometimes to improve the decision system the discretization of input data can be applied. In many cases such approach allows to obtain better classification results.
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Adversarial Authorship Attribution for Deobfuscation

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022
Wanyue Zhai, Jonathan Rusert, Zubair Shafiq, Padmini Srinivasan   +3 more
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