Journal ArticleDOI
Does Size Matter? Authorship Attribution, Small Samples, Big Problem
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TLDR
In this article, the authors aim to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise, and a few controlled tests for different sample lengths, languages, and genres are discussed and compared.Abstract:
The aim of this study is to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise. A few controlled tests for different sample lengths, languages, and genres are discussed and compared. Depending on the corpus used, the minimal sample length varied from 2,500 words (Latin prose) to 5,000 or so words (in most cases, including English, German, Polish, and Hungarian novels). Another observation is connected with the method of sampling: contrary to common sense, randomly excerpted ‘bags of words’ turned out to be much more effective than the classical solution, i.e. using original sequences of words (‘passages’) of desired size. Although the tests have been performed using the Delta method ( Burrows, J.F . (2002). ‘Delta’: a measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing , 17 (3): 267–87) applied to the most frequent words, some additional experiments have been conducted for support vector machines and k -NN applied to most frequent words, character 3-grams, character 4-grams, and parts-of-speech-tag 3-grams. Despite significant differences in overall attributive success rate between particular methods and/or style markers, the minimal amount of textual data needed for reliable authorship attribution turned out to be method-independent.read more
Citations
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A Computational Approach to Source Adaptation in Thomas Malory’s Morte Darthur
TL;DR: This paper conducted a stylometric analysis of the eight different sections of Thomas Malory's Morte Darthur, in order to identify differences between these sections and how they correspond to the language of the Old French and Middle English sources that Malory is known to have worked with in the different sections.
Exploring the Role of Emojis in Tweets for Authorship Attribution
TL;DR: This article used emoji rich features to perform authorship attribution of tweets and showed that targeting emojis in the feature set prompts a percent increase of at least 30% in the accuracy.
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Layer on layer. ‘Computational archaeology’ in 15th-century Middle Dutch historiography
TL;DR: To distinguish different authorial layers within a 15th-century chronicle, a rare medieval autograph or author’s copy, and to test specifically the validity of claims concerning the original composition of the text, John Burrows’ tried-and-tested Delta method was used.
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.
Journal IssueDOI
Computational methods in authorship attribution
TL;DR: 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.
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‘Delta’: a Measure of Stylistic Difference and a Guide to Likely Authorship
TL;DR: A new way of using the relative frequencies of the very common words for comparing written texts and testing their likely authorship, which offers a simple but comparatively accurate addition to current methods of distinguishing the most likely author of texts exceeding about 1,500 words in length.
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How variable may a constant be? Measures of lexical richness in perspective
TL;DR: The results suggest that the empirical trajectories tap into a considerable amount of authorial structure without, however, guaranteeing that spatial separation implies a difference in authorship.
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The State of Authorship Attribution Studies: Some Problems and Solutions
TL;DR: The statement, ’’Results of most non-traditional authorship attribution studies are not universally accepted as definitive,'' is explicated.