Szerkesztő:R4b6it/próbalap

A stilometria a nyelvi stílus tanulmányozásának egyik alkalmazási területe, általában az írott szövegek vonatkozásában, de sikeresen alkalmazták már zenei[1] és képzőművészeti (festészeti)[2] területeken is[3]. Egy másik elképzelés szerint nyelvészeti diszciplínaként definiálható, amely a statisztikai elemzést alkalmazza az irodalmi szövegekre a szerzők stílusát különböző kvantitatív jellemzőkkel kiértékelve.[4]

A stilometriát gyakran alkalmazzák az ismeretlen vagy vitatott szerzőjű művek szerzői azonosításában.[5] Csakúgy vannak jogi, mint tudományos és irodalmi alkalmazásai is, a Shakespare-szerzőségi vizsgálatoktól az igazságügyi nyelvészetig.

TörténeteSzerkesztés

A stilometria a szövegelemzés korábbi módszereiből nőtt ki, amelyek a hitelesség, szerzői identitás és egyebek bizonyítására irányultak.

A diszciplína újabb művelésének nagy lendületet adtak az angol reneszánsz dráma szerzői problémáinak vizsgálata körüli kutatások. A kutatók és az olvasók rájöttek, hogy a korszak bizonyos műveiben jól felismerhető nyelvi megoldások azonosíthatók, és megpróbálták ezeket ismeretlen vagy többszerzős művek szerzőinek beazonosításában felhasználni. A legkorábbi próbálkozások nem mindig voltak sikeresek: 1901-ben egy kutató megpróbálta John Fletcher egyik nyelvi preferenciáját, az "'em" rövidített forma használatát a "them" forma helyett felhasználni arra, hogy a közös műveikben elkülönítse az ő részeit a Philip Massinger által írottaktól, de tévedésből egy olyan kiadást használt, amelyben Massinger összes rövid "'em" formáját a szerkesztő kiegészítette "them"-re.[6]

A stilometria alapjait a lengyel filozófus Wincenty Lutosławski vetette meg a Principes de stylométrie (1890) c. művében. Lutosławski a módszerét arra használta, hogy Platón dialógusainak a koronológiáját felállítsa.[7]

A számítógépek és kapacitásuk fejlődése a nagy mennyiségű adat feldolgozásában nagyságrendekkel megnövelte az ilyen jellegű munkálatok sikerességét. A számítógépek nagy adatelemző kapacitása azonban nem garantálja a minőségi eredményeket. A '60-as évek elején A. Q. Morton tiszteletes a Szent Pálnak tulajdonított 14 újtestamentumi levélről azt derítette ki számítógépes elemzés segítségével, hogy hat különböző szerzőtől származik. Módszerének ellenőrzése James Joyce műveire alkalmazva azt az eredményt hozta, hogy az Ulysses, Joyce több nézőpontú, többstílusú remekműve öt különböző személy által íródott, akik közül láthatóan egyikük sem vett részt Joyce első regényének, az Ifjúkori önarcképnek a megalkotásában.[8]

Idővel azonban a kutatók finomítottak a módszereiken és megközelítésmódjukon, hogy jobb eredményekhez jussanak. Az egyik sikeres korai vizsgálat volt pl. a Frederick Mosteller és David Wallace nevéhez fűződő 12 The Federalist Papers vitatott szerzőségének a tisztázása.[9] Bár a kiinduló feltételezésekkel és a módszertannal kapcsolatos kérdések még mindig felmerülnek (és talán mindig is fel fognak merülni), ma már kevesen vitatják azt az alaptételt, hogy az írott szövegek nyelvészeti elemzése értékes információkkal és felismerésekkel szolgálhat. (Ez már a számítógépek megjelenése előtt is nyilvánvaló volt: a textuális/nyelvi megközelítés sikeres alkalmazása a Fletcher-kánonra Cyrus Hoy és mások által az 1950-es évek végén és a 60-as évek elején egyértelmű eredményeket hozott.)

AlkalmazásaiSzerkesztés

Applications of stylometry include literary studies, historical studies, social studies, gender studies, and many forensic cases and studies.[10][11] It can also be applied to computer code.[12] Stylometry can also be used to predict whether someone is a native or non native English speaker through their typing speed.[13]

Stylometry as a method is vulnerable to the distortion of text during revision.[14] There is also the case of the author adopting different styles in the course of his career as was demonstrated in the case of Plato, who chose different stylistic policies such as the those adopted for the early and middle dialogues addressing the Socratic problem.[15]

Aktuális kutatásokSzerkesztés

Modern stylometry draws heavily on the aid of computers for statistical analysis, artificial intelligence and access to the growing corpus of texts available via the Internet.[16] Software systems such as Signature[17] (freeware produced by Dr Peter Millican of Oxford University), JGAAP[18] (the Java Graphical Authorship Attribution Program—freeware produced by Dr Patrick Juola of Duquesne University), stylo[19][20] (an open-source R package for a variety of stylometric analyses, including authorship attribution, developed by Maciej Eder, Jan Rybicki and Mike Kestemont) and Stylene[21] for Dutch (online freeware by Prof Walter Daelemans of University of Antwerp and Dr Véronique Hoste of University of Ghent) make its use increasingly practicable, even for the non-expert.

Tudományos összejövetelek és eseményekSzerkesztés

Stylometric methods are discussed in several academic fields, mostly as a tangential field of application as with machine learning, natural language processing, and lexicography.

Igazságügyi nyelvészetSzerkesztés

The International Association of Forensic Linguists (IAFL) organises the Biennial Conference of the International Association of Forensic Linguists (13th edition in 2016 in Porto) and publishes The International Journal of Speech, Language and the Law with forensic stylistics as one of its central topics.

AAAISzerkesztés

The Association for the Advancement of Artificial Intelligence (AAAI) has hosted several events on subjective and stylistic analysis of text.[22][23][24]

PANSzerkesztés

PAN workshops (originally, plagiarism analysis, authorship identification, and near-duplicate detection, later more generally workshop on uncovering plagiarism, authorship, and social software misuse) organised since 2007 mainly in conjunction with information access conferences such as ACM SIGIR, FIRE, and CLEF. PAN formulates shared challenge tasks for plagiarism detection,[25] authorship identification,[26] author gender identification,[27] author profiling,[28] vandalism detection,[29] and other related text analysis tasks, many of which hinge on stylometry.

Érdekes esettanulmányokSzerkesztés

  • Around 1370 to 1070 BC, as recorded in the Book of Judges, one tribe identified members of another tribe in order to kill them by asking them to say the word Shibboleth which in the dialect of the intended victims sounded like "sibboleth".[30]
  • In 1439, Lorenzo Valla showed that the Donation of Constantine was a forgery, an argument based partly on a comparison of the Latin with that used in authentic 4th-century documents.
  • In 1952, the Swedish priest Dick Helander was elected bishop of Strängnäs. The campaign was competitive and Helander was accused of writing a series of a hundred-some anonymous libelous letters about other candidates to the electorate of the bishopric of Strängnäs. Helander was first convicted of writing the letters and lost his position as bishop but later partially exonerated. The letters were studied using a number of stylometric measures (and also typewriter characteristics) and the various court cases and further examinations, many contracted by Helander himself during the years up to his death in 1978 discussed stylometric methodology and its value as evidence in some detail.[31][32]
  • In 1975, after Ronald Reagan had served as governor of California, he began giving weekly radio commentaries syndicated to hundreds of stations. After his personal notes were made public on his 90th birthday in 2001, a study to determine which of those talks were written by him and which were written by various aides used stylostatistical methods.[33]
  • In 1996, the stylometric analysis of the controversial, pseudonymously authored book Primary Colors, performed by Vassar College professor Donald Foster[34] brought the field to the attention of a wider audience after correctly identifying the author as Joe Klein. (This case was only resolved after a handwriting analysis confirmed the authorship).
  • In 1996, stylometric methods were used to compare the Unabomber manifesto with letters written by one of the suspects, Theodor Kaczynski to his brother, which led to his apprehension and later conviction.[35]
  • In April 2015, researchers using stylometry techniques identified a play, Double Falsehood, as being the work of William Shakespeare.[36] Researchers analyzed 54 plays by Shakespeare and John Fletcher and compared average sentence length, studied the use of unusual words and quantified the complexity and psychological valence of its language.
  • In 2016, MacDonald P. Jackson, Emeritus Professor of English at the University of Auckland, New Zealand and a Fellow of the Royal Society of New Zealand, who had spent his entire academic career analyzing authorship attribution, wrote a book titled Who Wrote "the Night Before Christmas"?: Analyzing the Clement Clarke Moore Vs. Henry Livingston Question,[20], in which he evaluates the opposing arguments and, for the first time, uses the author-attribution techniques of modern computational stylistics to examine the long-standing controversy. Jackson employs a range of tests and introduces a new one, statistical analysis of phonemes; he concludes that Livingston is the true author of the classic work.
  • In 2017, Simon Fuller and James O'Sullivan published a study claiming that bestselling author James Patterson does not do any writing in his co-authored novels.[37][38][39] According to O'Sullivan, his collaboration with former U.S. president Bill Clinton, The President is Missing, is an exception to this rule.[40]
  • In 2017, a group of linguists, computer scientists, and scholars analysed the authorship of Elena Ferrante. Based on a corpus created at University of Padua containing 150 novels written by 40 authors, they analyzed Ferrante's style based on seven of her novels. They were able to compare her writing style with 39 other novelists using, for example, stylo.[19] The conclusion was the same for all of them: Domenico Starnone is the secret hand behind Elena Ferrante.[41]
  • In 2018, Mark Glickman, senior lecturer in statistics at Harvard University worked with Ryan Song, a former statistics student at Harvard, and Jason Brown, a professor at Dalhousie University in Nova Scotia, applied stylometry to find that, most likely, The Beatles' song "In My Life" was composed by John Lennon, but with a 50% chance that Paul McCartney wrote the middle eight.[42]

Adatok és módszerekSzerkesztés

Since stylometry has both descriptive use cases, used to characterise the content of a collection, and identificatory use cases, e.g. identifying authors or categories of texts, the methods used to analyse the data and features above range from those built to classify items into sets or to distribute items in a space of feature variation. Most methods are statistical in nature, such as cluster analysis and discriminant analysis, are typically based on philological data and features, and are fruitful application domains for modern machine learning approaches.

Whereas in the past, stylometry emphasized the rarest or most striking elements of a text, contemporary techniques can isolate identifying patterns even in common parts of speech. Most systems are based on lexical statistics, i.e. using the frequencies of words and terms in the text to characterise the text (or its author). In this context, unlike in information retrieval, the observed occurrence patterns of the most common words are more interesting than the topical terms which are less frequent.[50][51]

The primary stylometric method is the writer invariant: a property held in common by all texts, or at least all texts long enough to admit of analysis yielding statistically significant results, written by a given author. An example of a writer invariant is frequency of function words used by the writer.

In one such method, the text is analyzed to find the 50 most common words. The text is then broken into 5,000 word chunks and each of the chunks is analyzed to find the frequency of those 50 words in that chunk. This generates a unique 50-number identifier for each chunk. These numbers place each chunk of text into a point in a 50-dimensional space. This 50-dimensional space is flattened into a plane using principal components analysis (PCA). This results in a display of points that correspond to an author's style. If two literary works are placed on the same plane, the resulting pattern may show if both works were by the same author or different authors.

1. Gaussi statisztikaSzerkesztés

Stylometric data are distributed according the Zipf-Mandelbrot law. The distribution is extremely spiky and leptokurtic, reason why researchers had to turn their backs to statistics to solve e.g. authorship attribution problems. Nevertheless, usage of Gaussian statistics is perfectly possible by applying data transformation.[52]

2. Neurális hálózatokSzerkesztés

Neural networks, a special case of statistical machine learning methods, have been used to analyze authorship of texts. Text of undisputed authorship are used to train the neural network through processes such as backpropagation, where training error is calculated and used to update the process to increase accuracy. Through a process akin to non-linear regression, the network gains the ability to generalize its recognition ability to new texts to which it has not yet been exposed, classifying them to a stated degree of confidence. Such techniques were applied to the long-standing claims of collaboration of Shakespeare with his contemporaries Fletcher and Christopher Marlowe,[53][54] and confirmed the view, based on more conventional scholarship, that such collaboration had indeed taken place.

A 1999 study showed that a neural network program reached 70% accuracy in determining authorship of poems it had not yet analyzed. This study from Vrije Universiteit examined identification of poems by three Dutch authors using only letter sequences such as "den".[55]

A study used deep belief networks (DBN) for authorship verification model applicable for continuous authentication (CA).[56]

One problem with this method of analysis is that the network can become biased based on its training set, possibly selecting authors the network has more often analyzed.[55]

3. Genetikus algoritmusokSzerkesztés

The genetic algorithm is another machine learning technique used in stylometry. This involves a method that starts out with a set of rules. An example rule might be, "If but appears more than 1.7 times in every thousand words, then the text is author X". The program is presented with text and uses the rules to determine authorship. The rules are tested against a set of known texts and each rule is given a fitness score. The 50 rules with the lowest scores are thrown out. The remaining 50 rules are given small changes and 50 new rules are introduced. This is repeated until the evolved rules correctly attribute the texts.

4. Ritka párokSzerkesztés

One method for identifying style is called "rare pairs", and relies upon individual habits of collocation. The use of certain words may, for a particular author, idiosyncratically entail the use of other, predictable words.

Szerzői azonosítás az azonnali üzenetküldésbenSzerkesztés

The diffusion of Internet has shifted the authorship attribution attention towards online texts (web pages, blogs, etc.) electronic messages (e-mails, tweets, posts, etc.), and other types of written information that are far shorter than an average book, much less formal and more diverse in terms of expressive elements such as colors, layout, fonts, graphics, emoticons, etc. Efforts to take into account such aspects at the level of both structure and syntax were reported in.[57] In addition, content-specific and idiosyncratic cues (e.g., topic models and grammar checking tools) were introduced to unveil deliberate stylistic choices.[58]

Standard stylometric features have been employed to categorize the content of a chat over instant messaging,[59] or the behavior of the participants,[60] but attempts of identifying chat participants are still few and early. Furthermore, the similarity between spoken conversations and chat interactions has been neglected while being a key difference between chat data and any other type of written information.

Lásd mégSzerkesztés

JegyzetekSzerkesztés

  1. Westcott, Richard. „Making hit music into a science”, BBC News, 2006. június 15. 
  2. Internet Archive Wayback Machine, 2006. június 30. [2006. június 30-i dátummal az eredetiből archiválva]. (Hozzáférés: 2012. október 15.)
  3. Argamon, Shlomo, Kevin Burns, and Shlomo Dubnov, eds. The structure of style: algorithmic approaches to understanding manner and meaning. Springer Science & Business Media, 2010.
  4. Yang, Christopher C.. Intelligence and Security Informatics: IEEE ISI 2008 International Workshops: PAISI, PACCF and SOCO 2008, Taipei, Taiwan, June 17, 2008, Proceedings. Berlin: Springer Science & Business Media, 252. o. (2008. október 4.). ISBN 9783540691365 
  5. Chen, Hsinchun. Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2009, Bangkok, Thailand, April 27, 2009. Proceedings. Berlin: Springer Science & Business Media, 15. o. (2009. október 4.). ISBN 9783642013928 
  6. Samuel Schoenbaum, Internal evidence and Elizabethan dramatic authorship; an essay in literary history and method, p. 171.
  7. Lutoslawski, W. (1898. október 4.). „Principes de stylométrie appliqués à la chronologie des œuvres de Platon”. Revue des Études Grecques 11 (41), 61–81. o. DOI:10.3406/reg.1898.5847. ISSN 0035-2039.  
  8. Samuel Schoenbaum, Internal evidence and Elizabethan dramatic authorship; an essay in literary history and method, p. 196.
  9. Inference and Disputed Authorship: The Federalist. Reading, MA: Addison-Wesley (1964) 
  10. Sablon:Cite Book
  11. Forensic Science and Law: Investigative Applications in Criminal, Civil and Family Justice. CRC Press (2005. december 22.). ISBN 978-1-4200-5811-6 
  12. Claburn, Thomas. „FYI: AI tools can unmask anonymous coders from their binary executables”, The Register, 2018. március 16. (Hozzáférés ideje: 2018. augusztus 2.) 
  13. Brizan, David (2015. október 1.). „Utilizing linguistically enhanced keystroke dynamics to predict typist cognition and demographics”. International Journal of Human-Computer Studies 82, 57–68. o. DOI:10.1016/j.ijhcs.2015.04.005.  
  14. Alican, Necip Fikri. Rethinking Plato: A Cartesian Quest for the Real Plato. Amsterdam: Rodopi, 183. o. (2012. október 4.). ISBN 9789042035379 
  15. Rowe, Christopher. The Cambridge History of Greek and Roman Political Thought. Cambridge, UK: Cambridge University Press, 160. o. (2000. október 4.). ISBN 0521481368 
  16. Argamon, Shlomo, Jussi Karlgren, and James G. Shanahan. Stylistic analysis of text for information access. Papers from the workshop held in conjunction with the 28th Annual International ACM Conference on Research and Development in Information Retrieval, August 13–19, 2005, Salvador, Bahia, Brazil. Swedish institute of computer science, 2005.
  17. The Signature Stylometric System. PhiloComp. (Hozzáférés: 2014. január 3.)
  18. JGAAP. JGAAP, 2012. szeptember 4. (Hozzáférés: 2012. október 15.)
  19. a b The stylo for R package. Computational Stylistics Group, 2014. október 24. (Hozzáférés: 2014. október 24.)
  20. (2016) „Stylometry with R: a package for computational text analysis”. R Journal 8 (1), 107–121. o. DOI:10.32614/RJ-2016-007.  
  21. Sablon:Cite techreport
  22. Yan Qu, James Shanahan, and Janyce Wiebe. "Exploring attitude and affect in text: Theories and applications." AAAI Spring Symposium Technical report SS-04-07. AAAI Press, Menlo Park, CA. 2004.
  23. Jussi Karlgren, Björn Gambäck, and Pentti Kanerva. "Acquiring (and Using) Linguistic (and World) Knowledge for Information Access." (2002). AAAI Spring Symposium. Technical report SS-02-09. AAAI Press, Menlo Park, CA. 2002.
  24. Shlomo Argamon, Shlomo Dubnov, and Julie Jupp. "Style and Meaning in Language, Art, Music, and Design" (2004). AAAI Fall Symposium. Technical report FS-04-07.
  25. Potthast, Martin, Benno Stein, Alberto Barrón-Cedeño, and Paolo Rosso. "An evaluation framework for plagiarism detection." In Proceedings of the 23rd international conference on computational linguistics: Posters, pp. 997–1005. Association for Computational Linguistics, 2010.
  26. Stamatatos, Efstathios, Walter Daelemans, Ben Verhoeven, Patrick Juola, Aurelio López-López, Martin Potthast, and Benno Stein. "Overview of the Author Identification Task at PAN 2014." In CLEF (Working Notes), pp. 877–897. 2014.
  27. Rangel, Francisco, Paolo Rosso, Martin Potthast, and Benno Stein. "Overview of the 5th author profiling task at pan 2017: Gender and language variety identification in twitter." Working Notes Papers of the CLEF (2017).
  28. Rangel Pardo, Francisco Manuel, Fabio Celli, Paolo Rosso, Martin Potthast, Benno Stein, and Walter Daelemans. "Overview of the 3rd Author Profiling Task at PAN 2015." In CLEF 2015 Evaluation Labs and Workshop Working Notes Papers, pp. 1–8. 2015.
  29. Potthast, Martin, Benno Stein, and Teresa Holfeld. "Overview of the 1st International Competition on Wikipedia Vandalism Detection." In CLEF (Notebook Papers/LABs/Workshops). 2010.
  30. Sablon:Bibleverse
  31. Text processing text analysis and generation – text typology and attribution. Proceedings of Nobel symposium 51 / ed. by Sture Allén Stockholm : Almqvist & Wiksell international 1982 653 pp. Data linguistica ; 16 Nobel symposium ; 51 ISBN 91-22-00594-3
  32. Helander: An Authorship Attribution Case, 2003 (Hozzáférés: 2017. október 4.)
  33. Edoardo M. Airoldi (2007. július 1.). „Whose Ideas? Whose Words? Authorship of Ronald Reagan's Radio Addresses”. PS: Political Science & Politics 40 (3), 501–506. o. DOI:10.1017/S1049096507070874.  
  34. Author Unknown by Gavin McNett Salon November 2, 2000
  35. Belluck, Pam. „In Unabom Case, Pain for Suspect's Family”, The New York Times, 1996. április 10.. [2017. augusztus 10-i dátummal az eredetiből archiválva] (Hozzáférés ideje: 2008. július 5.) 
  36. Study finds a disputed Shakespeare play bears the master's mark. LATimes.com, 2015. április 10. (Hozzáférés: 2015. április 13.)
  37. (2017. október 4.) „Structure over Style: Collaborative Authorship and the Revival of Literary Capitalism”. Digital Humanities Quarterly 011 (1). (Hozzáférés ideje: 2017. április 20.)  
  38. Lane, Anthony. „Bill Clinton and James Patterson's Concussive Collaboration”, The New Yorker, 2018. június 18. (Hozzáférés ideje: 2018. június 7.) (amerikai angol nyelvű) 
  39. Why you don't need to write much to be the world's bestselling author. The Conversation, 2017. április 3. (Hozzáférés: 2017. április 20.)
  40. O'Sullivan, James: Bill Clinton and James Patterson are co-authors – but who did the writing? (angol nyelven). The Guardian , 2018. június 7. (Hozzáférés: 2018. június 7.)
  41. Jacques Savoy. Elena Ferrante Unmasked. https://www.researchgate.net/publication/320131096_Elena_Ferrante_Unmasked
  42. Peter Reuell. "You say John, I say Paul. But what does stylometry say?". https://news.harvard.edu/gazette/story/2018/09/harvard-statistician-examines-beatles-mystery/
  43. Un monstruo de la naturaleza llamado Lope (spanyol nyelven). abc , 2018. november 28. (Hozzáférés: 2019. augusztus 11.)
  44. Rastreadores digitales en el Siglo de Oro (spanyol nyelven). El Norte de Castilla , 2018. december 23. (Hozzáférés: 2019. augusztus 11.)
  45. Real, La Tribuna de Ciudad: Juan Ruiz de Alarcón aumenta su obra cinco siglos después (spanish nyelven). La Tribuna de Ciudad Real , 2019. július 9. (Hozzáférés: 2019. augusztus 11.)
  46. Chamberí, PSOE: PSOE | PSOE Chamberí | chamberí | suplemento cultural | domingo, 28 de julio 2019 | número 06 | Daniel Migueláñez | Pág nº 08 | El Holmes de la filología.. (Hozzáférés: 2019. augusztus 11.)
  47. Sor Juana Inés centró las 42 Jornadas de Teatro Clásico (európai spanyol nyelven). Lanza Digital , 2019. július 14. (Hozzáférés: 2019. augusztus 11.)
  48. 'La monja alférez' ya no es de Pérez de Montalbán, sino de Ruiz de Alarcón (spanyol nyelven). El Norte de Castilla , 2019. július 10. (Hozzáférés: 2019. augusztus 11.)
  49. McCarthy, Rachel. „Who wrote Wuthering Heights?” (angol nyelven). Digital Scholarship in the Humanities. DOI:10.1093/llc/fqaa031.  
  50. Biber, Douglas. Variation across speech and writing. Cambridge University Press, 1991.
  51. (1994. október 4.) „Recognizing Text Genres with Simple Metrics Using Discriminant Analysis”. Proceedings of the International Conference on Computational Linguistics 2, 1071. o. DOI:10.3115/991250.991324.  
  52. Van Droogenbroeck F.J., 'An essential rephrasing of the Zipf-Mandelbrot law to solve authorship attribution applications by Gaussian statistics' (2019) [1]
  53. [2] Neural Computation in Stylometry I: An Application to the Works of Shakespeare and Fletcher Matthews RAJ & Merriam TVN Lit Linguist Computing (1993) 8 (4): 203–209. doi: 10.1093/llc/8.4.203
  54. [3]Neural Computation in Stylometry II: An Application to the Works of Shakespeare and Marlowe Merriam TVN & Matthews RAJ Lit Linguist Computing (1994) 9 (1): 1–6
  55. a b JF HoornZ (2012. szeptember 3.). „Neural network identification of poets using letter sequences”. Literary and Linguistic Computing 14 (3), 311–338. o. DOI:10.1093/llc/14.3.311. (Hozzáférés ideje: 2012. október 15.)  
  56. (2017) „Authorship verification using deep belief network systems”. Int J Commun Syst. 30 (12), e3259. o. DOI:10.1002/dac.3259.  
  57. de Vel, O. (2001. december 1.). „Mining e-Mail Content for Author Identification Forensics”. SIGMOD Rec. 30 (4), 55–64. o. DOI:10.1145/604264.604272. ISSN 0163-5808.  
  58. Argamon, Shlomo (2009. február 1.). „Automatically Profiling the Author of an Anonymous Text”. Commun. ACM 52 (2), 119–123. o. DOI:10.1145/1461928.1461959. ISSN 0001-0782.  
  59. Classification of Instant Messaging Communications for Forensics Analysis – TechRepublic. TechRepublic . (Hozzáférés: 2016. január 26.)
  60. Zhou, L.. Can online behavior unveil deceivers? – an exploratory investigation of deception in instant messaging, 9 pp.–. o.. DOI: 10.1109/HICSS.2004.1265079 (2004. január 1.). ISBN 978-0-7695-2056-8 

HivatkozásokSzerkesztés

További olvasmányokSzerkesztés

See also the academic journal Literary and Linguistic Computing (published by the University of Oxford) and the Language Resources and Evaluation journal.

Külső hivatkozásokSzerkesztés

[[Category:Language varieties and styles]] [[:Kategória:Digitális bölcsészet]] [[Category:Quantitative linguistics]] [[Category:Authorship debates]] [[Category:Computational fields of study]]