Stylometry - Revision history

Conservative: /* Stylometry concepts */

2020-05-31T09:47:21Z

Stylometry concepts

Conservative at 07:39, May 31, 2020

2020-05-31T07:39:24Z

Conservative at 07:38, May 31, 2020

2020-05-31T07:38:52Z

Conservative at 07:38, May 31, 2020

2020-05-31T07:38:09Z

Conservative: /* Author obfuscation methods */

2020-05-31T06:13:37Z

Author obfuscation methods

Conservative: /* Author obfuscation methods */

2020-05-31T06:12:53Z

Author obfuscation methods

Conservative: /* Author obfuscation methods */

2020-05-31T06:06:38Z

Author obfuscation methods

Conservative: /* Author obfuscation methods */

2020-05-31T06:06:16Z

Author obfuscation methods

Conservative: /* Author obfuscation methods */

2020-05-31T06:05:04Z

Author obfuscation methods

Conservative: /* Author obfuscation methods */

2020-05-31T06:03:38Z

Author obfuscation methods

← Older revision		Revision as of 09:47, May 31, 2020
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	In literary criticism, close reading describes a sustained attention to the text, looking at word choice, syntax, and particular images, to reveal meaning. This methodology came out of New Criticism and does not rely on historical or biographical research, intending to analyze the complexities of the individual text instead.		In literary criticism, close reading describes a sustained attention to the text, looking at word choice, syntax, and particular images, to reveal meaning. This methodology came out of New Criticism and does not rely on historical or biographical research, intending to analyze the complexities of the individual text instead.
−	~~Distant reading~~
−
−	This is a concept coined by Franco Moretti that suggests that looking at the wider scope of literature, through larger computational or archival methods, can help us see larger trends and reveal previously obscured systems within literary study.

	'''Distant reading'''		'''Distant reading'''

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-[[File:Shakespeare f. Reuters.jpg|thumb|200px|In 1996, computer style analysis at the Shakespeare Clinic at Claremont College in California compared Shakespeare's writing to that of a corpus of twenty-six Elizabethan authors, including Bacon, Marlowe and Oxford. <ref>Ward Elliott and Robert Valenza, "[http://shakespeareauthorship.com/elval.html Was the Earl of Oxford the true Shakespeare? A Computer-aided analysis]" (1991)<br/>
+[[File:Shakespeare f. Reuters.jpg|thumb|200px|In 1996, computer style analysis at the [[Shakespeare]] Clinic at Claremont College in California compared Shakespeare's writing to that of a corpus of twenty-six Elizabethan authors, including Bacon, Marlowe and Oxford. <ref>Ward Elliott and Robert Valenza, "[http://shakespeareauthorship.com/elval.html Was the Earl of Oxford the true Shakespeare? A Computer-aided analysis]" (1991)<br/>
 Elliott and Valenza, "[http://eric.ed.gov/?id=EJ571023 And Then There Were None: Winnowing the Shakespeare Claimants]", ''Computers and the Humanities'', v30 n3 p191-245 1996. For a summary, see [http://www.shakespeareauthorship.com/ox6.html here].<br/>Elliott and Valenza, "[https://www.academia.edu/612631/Oxford_by_the_Numbers_What_Are_the_Odds_That_the_Earl_of_Oxford_Could_Have_Written_Shakespeares_Poems_and_Plays Oxford by the Numbers: What Are the Odds That the Earl of Oxford Could Have Written Shakespeare's Poems and Plays?]. (2004) "The odds that either could have written the other’s work are much lower than the odds of getting hit by lightning."</ref>]]
 According to the article ''Introduction to '''stylometry''' with Python'' by François Dominic Laramée:

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-[[File:Shakespeare f. Reuters.jpg|thumb|200px|In 1996, computer style analysis at the Shakespeare Clinic at Claremont College in California compared Shakespeare's writing to that of a corpus of twenty-six Elizabethan authors, including Bacon, Marlowe and Oxford. Ten style criteria were used to test the corpus, including use of relative clauses and the use of hyphenated compound words. Shakespeare's writing was found have a style distinct from that of every other author in the study. The authors were ranked according to how similar their styles were to that of Shakespeare. Oxford scored 22nd of the 26 claimants.<ref>Ward Elliott and Robert Valenza, "[http://shakespeareauthorship.com/elval.html Was the Earl of Oxford the true Shakespeare? A Computer-aided analysis]" (1991)<br/>
+[[File:Shakespeare f. Reuters.jpg|thumb|200px|In 1996, computer style analysis at the Shakespeare Clinic at Claremont College in California compared Shakespeare's writing to that of a corpus of twenty-six Elizabethan authors, including Bacon, Marlowe and Oxford. <ref>Ward Elliott and Robert Valenza, "[http://shakespeareauthorship.com/elval.html Was the Earl of Oxford the true Shakespeare? A Computer-aided analysis]" (1991)<br/>
 Elliott and Valenza, "[http://eric.ed.gov/?id=EJ571023 And Then There Were None: Winnowing the Shakespeare Claimants]", ''Computers and the Humanities'', v30 n3 p191-245 1996. For a summary, see [http://www.shakespeareauthorship.com/ox6.html here].<br/>Elliott and Valenza, "[https://www.academia.edu/612631/Oxford_by_the_Numbers_What_Are_the_Odds_That_the_Earl_of_Oxford_Could_Have_Written_Shakespeares_Poems_and_Plays Oxford by the Numbers: What Are the Odds That the Earl of Oxford Could Have Written Shakespeare's Poems and Plays?]. (2004) "The odds that either could have written the other’s work are much lower than the odds of getting hit by lightning."</ref>]]
 According to the article ''Introduction to '''stylometry''' with Python'' by François Dominic Laramée:

← Older revision		Revision as of 07:38, May 31, 2020
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		+	[[File:Shakespeare f. Reuters.jpg\|thumb\|200px\|In 1996, computer style analysis at the Shakespeare Clinic at Claremont College in California compared Shakespeare's writing to that of a corpus of twenty-six Elizabethan authors, including Bacon, Marlowe and Oxford. Ten style criteria were used to test the corpus, including use of relative clauses and the use of hyphenated compound words. Shakespeare's writing was found have a style distinct from that of every other author in the study. The authors were ranked according to how similar their styles were to that of Shakespeare. Oxford scored 22nd of the 26 claimants.<ref>Ward Elliott and Robert Valenza, "[http://shakespeareauthorship.com/elval.html Was the Earl of Oxford the true Shakespeare? A Computer-aided analysis]" (1991)<br/>
		+	Elliott and Valenza, "[http://eric.ed.gov/?id=EJ571023 And Then There Were None: Winnowing the Shakespeare Claimants]", ''Computers and the Humanities'', v30 n3 p191-245 1996. For a summary, see [http://www.shakespeareauthorship.com/ox6.html here].<br/>Elliott and Valenza, "[https://www.academia.edu/612631/Oxford_by_the_Numbers_What_Are_the_Odds_That_the_Earl_of_Oxford_Could_Have_Written_Shakespeares_Poems_and_Plays Oxford by the Numbers: What Are the Odds That the Earl of Oxford Could Have Written Shakespeare's Poems and Plays?]. (2004) "The odds that either could have written the other’s work are much lower than the odds of getting hit by lightning."</ref>]]
	According to the article ''Introduction to '''stylometry''' with Python'' by François Dominic Laramée:		According to the article ''Introduction to '''stylometry''' with Python'' by François Dominic Laramée:
	{{Cquote\|Stylometry is the quantitative study of literary style through computational distant reading methods. It is based on the observation that authors tend to write in relatively consistent, recognizable and unique ways. For example:		{{Cquote\|Stylometry is the quantitative study of literary style through computational distant reading methods. It is based on the observation that authors tend to write in relatively consistent, recognizable and unique ways. For example:

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 {{Cquote|Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents.<ref>Amancio DR (2015) [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136076 A Complex Network Approach to Stylometry]. PLoS ONE 10(8): e0136076. https://doi.org/10.1371/journal.pone.0136076</ref>}}
-== Author obfuscation methods ==
+== Author obfuscation methods and tools ==
 The are a number of free online article rewriters or paid programs which rewrite articles. The most rudimentary programs simply replace words with synonyms. More advanced programs change the complexity of the sentence structures.

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 The abstract for the 2019 journal article entitled ''A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X'' published in ''Proceedings on Privacy Enhancing Technologies'' indicates:
 {{Cquote|...a genetic algorithm based random search framework called Mutant-X which can automatically obfuscate text to successfully evade attribution while keeping the semantics of the obfuscated text similar to the original text. Specifically, Mutant-X sequentially makes changes in the text using mutation and crossover techniques while being guided by a fitness function that takes into account both attribution probability and semantic relevance. While Mutant-X requires black-box knowledge of the adversary’s classifier, it does not require any additional training data and also works on documents of any length. We evaluate Mutant-X against a variety of authorship attribution methods on two different text corpora. Our results show that Mutant-X can decrease the accuracy of state-of-the-art authorship attribution methods by as much as 64% while preserving the semantics much better than existing automated authorship obfuscation approaches.<ref>[https://content.sciendo.com/downloadpdf/journals/popets/2019/4/article-p54.pdf ''A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X''] by Asad Mahmood, Faizan Ahmad, Zubair Shafiq, Padmini Srinivasan, and Fareed Zaffar, ''Proceedings on Privacy Enhancing Technologies''; 2019 (4):54–71</ref>}}
 == See also ==

← Older revision		Revision as of 06:03, May 31, 2020
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	The most rudimentary programs simply replace words with synonyms. More advanced programs change the complexity of the sentence structures.		The most rudimentary programs simply replace words with synonyms. More advanced programs change the complexity of the sentence structures.
		+
		+	One of the most advanced author obfuscation tools developed is Mutant-X.<ref>[https://github.com/asad1996172/Mutant-X Mutant X - Github]</ref>
		+
		+	The abstract for the 2019 journal article entitled ''A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X'' published in ''Proceedings on Privacy Enhancing Technologies'' indicates:
		+	{{Cquote\|...a genetic algorithm based random search framework called Mutant-X which can automatically obfuscate text to successfully evade attribution while keeping the semantics of the obfuscated text similar to the original text. Specifically, Mutant-X sequentially makes changes in the text using mutation and crossover techniques while being guided by a fitness function that takes into account both attribution
		+	probability and semantic relevance. While Mutant-X requires black-box knowledge of the adversary’s classifier, it does not require any additional training data and also works on documents of any length. We evaluate Mutant-X against a variety of authorship attribution methods on two different text corpora. Our results show that Mutant-X can decrease the accuracy of state-of-the-art authorship attribution methods by
		+	as much as 64% while preserving the semantics much better than existing automated authorship obfuscation approaches.<ref>[https://content.sciendo.com/downloadpdf/journals/popets/2019/4/article-p54.pdf ''A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X''] by Asad Mahmood, Faizan Ahmad, Zubair Shafiq, Padmini Srinivasan, and Fareed Zaffar, ''Proceedings on Privacy Enhancing Technologies''; 2019 (4):54–71</ref>}}

	== See also ==		== See also ==