The State of Non-Traditional Authorship Attribution Studies—2012: Some Problems and Solutions

Rudman, Joseph

doi:10.1080/0013838x.2012.668785

Cited by 59 publications

(58 citation statements)

References 26 publications

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“…We can also rely on proven evaluation methods and methodological principles for comparing features and methods. Systematic studies in such a framework will go a long way in coming up to Rudman's [14,13] criticism that after more than 40 years of research and almost a thousand papers (many more counting conference contributions), modern authorship studies "have not yet passed a 'shake-down' phase and entered one marked by solid, scientific, and steadily progressing studies. "…”

Section: Meta-knowledge Extraction From Textmentioning

confidence: 99%

Explanation in Computational Stylometry

Daelemans

2013

Lecture Notes in Computer Science

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Abstract. Computational stylometry, as in authorship attribution or profiling, has a large potential for applications in diverse areas: literary science, forensics, language psychology, sociolinguistics, even medical diagnosis. Yet, many of the basic research questions of this field are not studied systematically or even at all. In this paper we will go into these problems, and suggest that a reinterpretation of current and historical methods in the framework and methodology of machine learning of natural language processing would be helpful. We also argue for more attention in research for explanation in computational stylometry as opposed to purely quantitative evaluation measures and propose a strategy for data collection and analysis for achieving progress in computational stylometry. We also introduce a fairly new application of computational stylometry in internet security. Meta-knowledge Extraction from TextThe form of a text is determined by many factors. Content plays a role (the topic of a text determines in part its vocabulary), text type (genre, register) is important and will determine part of the writing style, but also psychological and sociological aspects of the author of the text will be sources of stylistic language variation. These psychological factors include personality, mental health, and being a native speaker or not; sociological factors include age, gender, education level, and region of language acquisition.Writing style is a combination of consistent decisions in language production at different linguistic levels (lexical choice, syntactic structures, discourse coherence, ...) that is linked to specific authors or author groups such as male authors or teenage authors. It remains to be seen whether this link is consistent over time and whether there are style features that are unconscious and cannot be controlled, as some researchers have argued. The basic research question for computational stylometry seems then to describe and explain the causal relations between psychological and sociological properties of authors on the one hand, and their writing style on the other. These theories can be used to develop systems that generate text in a particular style, or perhaps more usefully, systems that detect the identity of authors (authorship attribution and verification) or some of their psychological or sociological properties (profiling) from text.A limit hypothesis arising from this definition is that style is unique for an individual, like her fingerprint, earprint or genome. This has been called the human stylome hypothesis:

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Section: Meta-knowledge Extraction From Textmentioning

confidence: 99%

Explanation in Computational Stylometry

Daelemans

2013

Lecture Notes in Computer Science

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“…More than 1.000 individual style markers have been proposed for usage in stylometry [16]. They range from lexical features, e.g.…”

Section: Plagiarism Detection Approachesmentioning

confidence: 99%

Comparative evaluation of text- and citation-based plagiarism detection approaches using guttenplag

Gipp

Meuschke²,

Beel

2011

Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries

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Various approaches for plagiarism detection exist. All are based on more or less sophisticated text analysis methods such as string matching, fingerprinting or style comparison. In this paper a new approach called Citation-based Plagiarism Detection is evaluated using a doctoral thesis [8], in which a volunteer crowd-sourcing project called GuttenPlag [1] identified substantial amounts of plagiarism through careful manual inspection. This new approach is able to identify similar and plagiarized documents based on the citations used in the text. It is shown that citation-based plagiarism detection performs significantly better than text-based procedures in identifying strong paraphrasing, translation and some idea plagiarism. Detection rates can be improved by combining citation-based with text-based plagiarism detection.

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“…Approximately more than 1.000 individual style markers [38] have been proposed for stylometry, most can be classified as falling into one of the following categories [46]: In previous papers [4,15] we initially proposed employing citation analysis for PD and presented results of initial studies. Citation-based Plagiarism Detection (CbPD) is a fundamentally different approach compared to the text-based similarity evaluations described above.…”

Section: String Matching Proceduresmentioning

confidence: 99%

Citation pattern matching algorithms for citation-based plagiarism detection

Gipp

Meuschke

2011

Proceedings of the 11th ACM Symposium on Document Engineering

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Plagiarism Detection Systems have been developed to locate instances of plagiarism e.g. within scientific papers. Studies have shown that the existing approaches deliver reasonable results in identifying copy&paste plagiarism, but fail to detect more sophisticated forms such as paraphrased, translated or idea plagiarism. The authors of this paper demonstrated in recent studies [4,15] that the detection rate can be significantly improved by not only relying on text analysis, but by additionally analyzing the citations of a document. Citations are valuable language independent markers that are similar to a fingerprint. In fact, our examinations of real world cases have shown that the order of citations in a document often remains similar even if the text has been strongly paraphrased or translated in order to disguise plagiarism. This paper introduces three algorithms and discusses their suitability for the purpose of Citation-based Plagiarism Detection. Due to the numerous ways in which plagiarism can occur, these algorithms need to be versatile. They must be capable of detecting transpositions, scaling and combinations in a local and global form. The algorithms are coined Greedy Citation Tiling, Citation Chunking and Longest Common Citation Sequence. The evaluation showed that common forms of plagiarism can be detected reliably if these algorithms are combined.

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The State of Non-Traditional Authorship Attribution Studies—2012: Some Problems and Solutions

Cited by 59 publications

References 26 publications

Explanation in Computational Stylometry

Explanation in Computational Stylometry

Comparative evaluation of text- and citation-based plagiarism detection approaches using guttenplag

Citation pattern matching algorithms for citation-based plagiarism detection

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