Non-traditional Authorship Attribution Studies in the Historia Augusta: Some Caveats

Rudman, Joseph

doi:10.1093/llc/13.3.151

Cited by 32 publications

(8 citation statements)

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“…This bias might be used to find unique scribal idiolects (Kestemont and Dalen-Oskam, 2009;Thaisen, 2011); it can be also subjected to automatic disambiguation of spelling variants (Craig andWhipp, 2010, Gotscharek et al, 2011). In most approaches, how- ever, there is no sufficient awareness of potential systematic error (Rudman, 1998a).…”

Section: Noise In Word Frequenciesmentioning

confidence: 99%

“…The problem of systematic errors in stylometry, and particularly in non-traditional authorship attribution, has already been discussed. Rudman (1998aRudman ( , 1998bRudman ( , 2003 has formulated a number of caveats concerning different issues in non-traditional authorship attribution, including possible pitfalls in corpus preparation. Noecker et al (2008), in their attempt to test the impact of optical character recognition (OCR) errors on attribution accuracy, have observed that moderate damage of input texts does not affect the results significantly.…”

Section: Introductionmentioning

confidence: 99%

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Mind your corpus: systematic errors in authorship attribution

Eder

2013

Literary and Linguistic Computing

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In computational stylistics, any influence of unwanted noise-e.g. caused by an untidilyprepared corpus-might lead to biased or false results. Relying on contaminated data is quite similar to using dirty test tubes in a laboratory: it inescapably means falling into systematic error. An important question is what degree of nonchalance is acceptable to obtain sufficiently reliable results. The present study attempts to verify the impact of unwanted noise in a series of experiments conducted on several corpora of English, German, Polish, Ancient Greek and Latin prose texts. In 100 iterations, a given corpus was gradually damaged, and controlled tests for authorship were applied. The first experiment was designed to show the correlation between a dirty corpus and attribution accuracy. The second was aimed to test how disorder in word frequencies-produced by scribal and/or editorial modifications-affects the attribution abilities of particular corpora. The goal of the third experiment was to test how much 'authorial' data a given text needs to have in order to trace authorial fingerprint through a mass of external quotations.

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Section: Noise In Word Frequenciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mind your corpus: systematic errors in authorship attribution

Eder

2013

Literary and Linguistic Computing

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“…Throughout this story of stylometry's coming-of-age, it is important to emphasize that it presents no threat to traditional scholarship. Stylometric evidence must always be weighed in the balance along with that provided by more conventional studies made by literary scholars (Rudman 1998). Although as yet, no definitive methodology or technique has emerged, statisticians are coming closer to stylometry's "holy grail," a fully automated identifier that may successfully be applied to all genres, languages and eras.~R eferences and Further Reading…”

Section: The Futurementioning

confidence: 99%

Who Was the Author? An Introduction to Stylometry

Holmes¹,

Kardos²

2003

CHANCE

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“…There appears dissension among leading Shakespearean authorship attribution scholars about an agreed method (Rudman, 2016 ), but the most successful and robust methods rely on low-level information such as character n-grams or auxiliary word (function words and stop words such as articles and prepositions) frequencies (Stamatatos, 2009 ). The premier work in evaluating authorship in the 16th to mid-17th centuries includes MacDonald P. Jackson, Brian Vickers, and Hugh Craig (Segarra et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Using Shakespeare's Sotto Voce to Determine True Identity From Text

2018

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Little is known of the private life of William Shakespeare, but he is famous for his collection of plays and poems, even though many of the works attributed to him were published anonymously. Determining the identity of Shakespeare has fascinated scholars for 400 years, and four significant figures in English literary history have been suggested as likely alternatives to Shakespeare for some disputed works: Bacon, de Vere, Stanley, and Marlowe. A myriad of computational and statistical tools and techniques have been used to determine the true authorship of his works. Many of these techniques rely on basic statistical correlations, word counts, collocated word groups, or keyword density, but no one method has been decided on. We suggest that an alternative technique that uses word semantics to draw on personality can provide an accurate profile of a person. To test this claim, we analyse the works of Shakespeare, Christopher Marlowe, and Elizabeth Cary. We use Word Accumulation Curves, Hierarchical Clustering overlays, Principal Component Analysis, and Linear Discriminant Analysis techniques in combination with RPAS, a multi-faceted text analysis approach that draws on a writer's personality, or self to identify subtle characteristics within a person's writing style. Here we find that RPAS can separate the known authored works of Shakespeare from Marlowe and Cary. Further, it separates their contested works, works suspected of being written by others. While few authorship identification techniques identify self from the way a person writes, we demonstrate that these stylistic characteristics are as applicable 400 years ago as they are today and have the potential to be used within cyberspace for law enforcement purposes.

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Non-traditional Authorship Attribution Studies in the Historia Augusta: Some Caveats

Cited by 32 publications

References 0 publications

Mind your corpus: systematic errors in authorship attribution

Mind your corpus: systematic errors in authorship attribution

Who Was the Author? An Introduction to Stylometry

Using Shakespeare's Sotto Voce to Determine True Identity From Text

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