Relevant bibliographies by topics / Authorship attribution

Academic literature on the topic 'Authorship attribution'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Authorship attribution"

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Juola, Patrick. "Authorship Attribution." Foundations and Trends® in Information Retrieval 1, no. 3 (2007): 233–334. http://dx.doi.org/10.1561/1500000005.

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Holmes, David I. "Authorship attribution." Computers and the Humanities 28, no. 2 (April 1994): 87–106. http://dx.doi.org/10.1007/bf01830689.

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Kalgutkar, Vaibhavi, Ratinder Kaur, Hugo Gonzalez, Natalia Stakhanova, and Alina Matyukhina. "Code Authorship Attribution." ACM Computing Surveys 52, no. 1 (February 28, 2019): 1–36. http://dx.doi.org/10.1145/3292577.

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Altakrori, Malik H., Farkhund Iqbal, Benjamin C. M. Fung, Steven H. H. Ding, and Abdallah Tubaishat. "Arabic Authorship Attribution." ACM Transactions on Asian and Low-Resource Language Information Processing 18, no. 1 (January 8, 2019): 1–51. http://dx.doi.org/10.1145/3236391.

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HaCohen-Kerner, Yaakov, and Orr Margaliot. "AUTHORSHIP ATTRIBUTION OFRESPONSAUSING CLUSTERING." Cybernetics and Systems 45, no. 6 (August 18, 2014): 530–45. http://dx.doi.org/10.1080/01969722.2014.945311.

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Grabchak, M., Z. Zhang, and D. T. Zhang. "Authorship Attribution Using Entropy." Journal of Quantitative Linguistics 20, no. 4 (November 2013): 301–13. http://dx.doi.org/10.1080/09296174.2013.830551.

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Mahmood, Asad, Faizan Ahmad, Zubair Shafiq, Padmini Srinivasan, and Fareed Zaffar. "A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X." Proceedings on Privacy Enhancing Technologies 2019, no. 4 (October 1, 2019): 54–71. http://dx.doi.org/10.2478/popets-2019-0058.

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Abstract Stylometric authorship attribution aims to identify an anonymous or disputed document’s author by examining its writing style. The development of powerful machine learning based stylometric authorship attribution methods presents a serious privacy threat for individuals such as journalists and activists who wish to publish anonymously. Researchers have proposed several authorship obfuscation approaches that try to make appropriate changes (e.g. word/phrase replacements) to evade attribution while preserving semantics. Unfortunately, existing authorship obfuscation approaches are lacking because they either require some manual effort, require significant training data, or do not work for long documents. To address these limitations, we propose 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. While Mutant-X advances the state-of-the-art in automated authorship obfuscation, we find that it does not generalize to a stronger threat model where the adversary uses a different attribution classifier than what Mutant-X assumes. Our findings warrant the need for future research to improve the generalizability (or transferability) of automated authorship obfuscation approaches.
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Seroussi, Yanir, Ingrid Zukerman, and Fabian Bohnert. "Authorship Attribution with Topic Models." Computational Linguistics 40, no. 2 (June 2014): 269–310. http://dx.doi.org/10.1162/coli_a_00173.

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Authorship attribution deals with identifying the authors of anonymous texts. Traditionally, research in this field has focused on formal texts, such as essays and novels, but recently more attention has been given to texts generated by on-line users, such as e-mails and blogs. Authorship attribution of such on-line texts is a more challenging task than traditional authorship attribution, because such texts tend to be short, and the number of candidate authors is often larger than in traditional settings. We address this challenge by using topic models to obtain author representations. In addition to exploring novel ways of applying two popular topic models to this task, we test our new model that projects authors and documents to two disjoint topic spaces. Utilizing our model in authorship attribution yields state-of-the-art performance on several data sets, containing either formal texts written by a few authors or informal texts generated by tens to thousands of on-line users. We also present experimental results that demonstrate the applicability of topical author representations to two other problems: inferring the sentiment polarity of texts, and predicting the ratings that users would give to items such as movies.
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Grabchak, Michael, Lijuan Cao, and Zhiyi Zhang. "Authorship Attribution Using Diversity Profiles." Journal of Quantitative Linguistics 25, no. 2 (July 14, 2017): 142–55. http://dx.doi.org/10.1080/09296174.2017.1343268.

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Koppel, Moshe, Jonathan Schler, and Shlomo Argamon. "Authorship attribution in the wild." Language Resources and Evaluation 45, no. 1 (January 13, 2010): 83–94. http://dx.doi.org/10.1007/s10579-009-9111-2.

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Dissertations / Theses on the topic "Authorship attribution"

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Calarota, Gabriele. "On Authorship Attribution." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22809/.

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Authorship attribution is the process of identifying the author of a given text and from the machine learning perspective, it can be seen as a classification problem. In the literature, there are a lot of classification methods for which feature extraction techniques are conducted. In this thesis, we explore information retrieval techniques such as Doc2Vec and other useful feature selection and extraction techniques for a given text with different classifiers. The main purpose of this work is to lay the foundations of feature extraction techniques in authorship attribution. At the end of this work, we show how we compared our results with related works and how we managed to improve, to the best of our knowledge, the results on a particular dataset, very known in this field.
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Honaker, Randale J. "Novel topic authorship attribution." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5761.

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Approved for public release; distribution is unlimited
The practice of using statistical models in predicting authorship (so-called author-attribution models) is long established. Several recent authorship attribution studies have indicated that topic-specific cues impact author-attribution machine learning models. The arrival of new topics should be anticipated rather than ignored in an author attribution evaluation methodology; a model that relies heavily on topic cues will be problematic in deployment settings where novel topics are common. In order to effectively deal with novel topics, we create author and topic vectors and attempt to project out the topic influences from each document. Although our experiments did not validate our assumptions, they do point out a possible problem with a common assumption in authorship attribution research.
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Lalla, Himal. "E-mail forensic authorship attribution." Thesis, University of Fort Hare, 2010. http://hdl.handle.net/10353/360.

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E-mails have become the standard for business as well as personal communication. The inherent security risks within e-mail communication present the problem of anonymity. If an author of an e-mail is not known, the digital forensic investigator needs to determine the authorship of the e-mail using a process that has not been standardised in the e-mail forensic field. This research project examines many problems associated with e-mail communication and the digital forensic domain; more specifically e-mail forensic investigations, and the recovery of legally admissible evidence to be presented in a court of law. The Research Methodology utilised a comprehensive literature review in combination with Design Science which results in the development of an artifact through intensive research. The Proposed E-Mail Forensic Methodology is based on the most current digital forensic investigation process and further validation of the process was established via expert reviews. The opinions of the digital forensic experts were an integral portion of the validation process which adds to the credibility of the study. This was performed through the aid of the Delphi technique. This Proposed E-Mail Forensic Methodology adopts a standardised investigation process applied to an e-mail investigation and takes into account the South African perspective by incorporating various checks with the laws and legislation. By following the Proposed E-mail Forensic Methodology, e-mail forensic investigators can produce evidence that is legally admissible in a court of law.
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Gerritsen, Corey M. (Corey Metcalf) 1979. "Authorship attribution using lexical attraction." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87414.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 56-57).
by Corey M. Gerritsen.
M.Eng.and S.B.
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Tennyson, Matthew Francis. "Authorship Attribution of Source Code." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/322.

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Authorship attribution of source code is the task of deciding who wrote a program, given its source code. Applications include software forensics, plagiarism detection, and determining software ownership. A number of methods for the authorship attribution of source code have been presented in the past. A review of those existing methods is presented, while focusing on the two state-of-the-art methods: SCAP and Burrows. The primary goal was to develop a new method for authorship attribution of source code that is even more effective than the current state-of-the-art methods. Toward that end, a comparative study of the methods was performed in order to determine their relative effectiveness and establish a baseline. A suitable set of test data was also established in a manner intended to support the vision of a universal data set suitable for standard use in authorship attribution experiments. A data set was chosen consisting of 7,231 open-source and textbook programs written in C++ and Java by thirty unique authors. The baseline study showed both the Burrows and SCAP methods were indeed state-of-the-art. The Burrows method correctly attributed 89% of all documents, while the SCAP method correctly attributed 95%. The Burrows method inherently anonymizes the data by stripping all comments and string literals, while the SCAP method does not. So the methods were also compared using anonymized data. The SCAP method correctly attributed 91% of the anonymized documents, compared to 89% by Burrows. The Burrows method was improved in two ways: the set of features used to represent programs was updated and the similarity metric was updated. As a result, the improved method successfully attributed nearly 94% of all documents, compared to 89% attributed in the baseline. The SCAP method was also improved in two ways: the technique used to anonymize documents was changed and the amount of information retained in the source code author profiles was determined differently. As a result, the improved method successfully attributed 97% of anonymized documents and 98% of non-anonymized documents, compared to 91% and 95% that were attributed in the baseline, respectively. The two improved methods were used to create an ensemble method based on the Bayes optimal classifier. The ensemble method successfully attributed nearly 99% of all documents in the data set.
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Grant, T. D. "Authorship attribution in a forensic context." Thesis, University of Birmingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529439.

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This thesis develops a quantitative method for forensic authorship attribution. The principal constraint, that the method be scientific according to the Daubert criteria, necessitates that the conclusions drawn about authorship problems must be made to a known degree of certainty. In response, the theoretical part of the thesis establishes the criteria for a sound method in authorship attribution as relying on valid, reliable markers of authorship and the development of an explicit and specific sampling strategy. The main empirical part of the thesis draws potential markers of authorship from the literature and tests them against a specially constructed General Authorship Corpus. The resulting battery of reliable markers of authorship includes word and sentence length statistics and word-frequency measures. A series of worked examples with decreasing number of texts demonstrates the method and tests its limits, showing positive attributions where possible and no false attributions even when comparison data is limited. In addition to the development and application of the battery of valid, reliable markers of authorship, the role of stylistic idiosyncrasies in attribution is discussed and developed as a secondary strategy. Possibilities for the statistical presentation of results are considered and a Bayesian approach is proffered as the most desirable
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Pires, David Laranjo. "Authorship attribution using co-occurrence networks." Master's thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/30831.

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Atribuição de Autoria utlizando Redes de Co-Ocorrencia Nesta tese é abordada a tarefa de Atribuição de Autoria como uma tarefa de classificação. As metodologias utilizadas representam textos em grafos. Destes, várias medidas são extraídas, sendo utilizadas como amostras para o classificador. Já existem alguns trabalhos que também se focam nesta metodologia. Esta tese foca-se num método que divide o texto em várias partes e trata cada uma como um grafo. Deste, são extraídas as medidas, que são tratadas como uma série temporal, da qual são extraídos momentos. Assim, os momentos compõem o vetor final, representativo de todo o texto. A partir da metodologia aqui descrita surgem mais duas variações. A primeira variação omite o passo das séries temporais, e, por consequência, as várias medidas de cada grafo são utilizadas diretamente como amostras. A segunda variação representa todo o texto como um só grafo. As metodologias são testadas com corpus em Inglês e Português, com número variado de textos; Abstract: Authorship Attribution using Co-Occurrence Networks This thesis approaches the task of Authorship Attribution as a classification task. This is done using methodologies that represent text documents in graphs, from which several measures are extracted, to be used as samples for the classifier. There have been some works that also focus on this methodology. This thesis focuses on a methodology which splits the texts in multiple parts and treats each as a separate graph, from which measures are extracted. Each graph’s measures are treated as a time-series and moments are extracted. These moments make the final vector, representative of the entire text. This methodology is explored and extended with 2 variations. The first variation skips the time-series step, resulting in the various measures from each graph being used directly as samples. The second variation models the entire text as one graph. The methodologies are tested in corpus in both English and Portuguese, with varying number of texts.
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Gopalakrishnan, Sridharan. "Authorship Attribution based on Grammar Signatures." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368026620.

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Caver, Johnnie F. "Novel topic impact on authorship attribution." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Dec/09Dec%5FCaver.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, December 2009.
Thesis Advisor(s): Schein, Andrew I. ; Martell, Craig H. "December 2009." Description based on title screen as viewed on February 01, 2010. Author(s) subject terms: Authorship detection, topic detection, author-topic correlation, topic-author correlation, maximum entropy, New York Times Annotated Corpus. Includes bibliographical references (p. 61-63). Also available in print.
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Zhao, Ying, and ying zhao@rmit edu au. "Effective Authorship Attribution in Large Document Collections." RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080730.162501.

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Techniques that can effectively identify authors of texts are of great importance in scenarios such as detecting plagiarism, and identifying a source of information. A range of attribution approaches has been proposed in recent years, but none of these are particularly satisfactory; some of them are ad hoc and most have defects in terms of scalability, effectiveness, and computational cost. Good test collections are critical for evaluation of authorship attribution (AA) techniques. However, there are no standard benchmarks available in this area; it is almost always the case that researchers have their own test collections. Furthermore, collections that have been explored in AA are usually small, and thus whether the existing approaches are reliable or scalable is unclear. We develop several AA collections that are substantially larger than those in literature; machine learning methods are used to establish the value of using such corpora in AA. The results, also used as baseline results in this thesis, show that the developed text collections can be used as standard benchmarks, and are able to clearly distinguish between different approaches. One of the major contributions is that we propose use of the Kullback-Leibler divergence, a measure of how different two distributions are, to identify authors based on elements of writing style. The results show that our approach is at least as effective as, if not always better than, the best existing attribution methods-that is, support vector machines-for two-class AA, and is superior for multi-class AA. Moreover our proposed method has much lower computational cost and is cheaper to train. Style markers are the key elements of style analysis. We explore several approaches to tokenising documents to extract style markers, examining which marker type works the best. We also propose three systems that boost the AA performance by combining evidence from various marker types, motivated from the observation that there is no one type of marker that can satisfy all AA scenarios. To address the scalability of AA, we propose the novel task of authorship search (AS), inspired by document search and intended for large document collections. Our results show that AS is reasonably effective to find documents by a particular author, even within a collection consisting of half a million documents. Beyond search, we also propose the AS-based method to identify authorship. Our method is substantially more scalable than any method published in prior AA research, in terms of the collection size and the number of candidate authors; the discrimination is scaled up to several hundred authors.
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Books on the topic "Authorship attribution"

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Juola, Patrick. Authorship attribution. Boston: Now, 2008.

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Iqbal, Farkhund, Mourad Debbabi, and Benjamin C. M. Fung. Machine Learning for Authorship Attribution and Cyber Forensics. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61675-5.

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Uesaka, Ayaka, Gen Tsuchiyama, and Masakatsu Murakami. Keiryō bunkengaku no shatei: The computational authorship attribution. Tōkyō: Bensei Shuppan, 2016.

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Michelangelo's drawings: The science of attribution. New Haven: Yale University Press, 1991.

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Allegro, Roberto. L'ultimo Vivaldi: Note storiche per una riscoperta vivaldiana : Sonate RV 809 e RV 820. Monza: Casa musicale Eco, 2016.

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Roos, Gerd, and Paolo Baldacci. Giorgio De Chirico: Interno metafisico (Nature morte) : luglio-agosto 1933, già Collezione Emilio e Maria Jesi, Milano. Milano: Scalpendi, 2011.

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Antonio, Fanna, Talbot Michael, and Istituto italiano Antonio Vivaldi, eds. Vivaldi, vero e falso: Problemi di attribuzione. Firenze: L.S. Olschki, 1992.

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Company i Climent, Ximo, 1953- author, Vos, Cornelis de, 1584 or 1585-1651, Snyders Frans 1579-1657, and Iveagh Bequest, Kenwood (London, England), eds. Cornelis de Vos: Pareja de granjeros camino del mercado : Una versión desconocida de la obra de Frans Snyders y Cornelis de Vos en Kenwood House-The Iveagh Bequest. Lleida: Universitat de Lleida, 2014.

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Varzi, Salvatore. Sfidando l'ignoto: Antonello e l'enigma di Cefalù. Palermo: Torri del vento edizioni, 2017.

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Foster, Donald W. Elegy by W.S.: A study in attribution. Newark: University of Delaware Press, 1989.

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Book chapters on the topic "Authorship attribution"

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Alrabaee, Saed, Mourad Debbabi, Paria Shirani, Lingyu Wang, Amr Youssef, Ashkan Rahimian, Lina Nouh, Djedjiga Mouheb, He Huang, and Aiman Hanna. "Authorship Attribution." In Advances in Information Security, 211–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34238-8_9.

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Sharpe, Will. "Authorship and Attribution." In William Shakespeare and Others, 641–745. London: Macmillan Education UK, 2013. http://dx.doi.org/10.1007/978-1-137-27145-7_11.

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Patil, Suman, Sandeep Varma Nadimpalli, and Pavithra K. Yadav. "Email Authorship Attribution." In Lecture Notes in Networks and Systems, 451–57. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3765-9_47.

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Marchenko, Oleksandr, Anatoly Anisimov, Andrii Nykonenko, Tetiana Rossada, and Egor Melnikov. "Authorship Attribution System." In Natural Language Processing and Information Systems, 227–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59569-6_27.

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Juola, Patrick. "Authorship Analysis and Attribution." In Encyclopedia of Big Data, 58–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_522.

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Juola, Patrick. "Authorship Analysis and Attribution." In Encyclopedia of Big Data, 1–3. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-32001-4_522-1.

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Iqbal, Farkhund, Mourad Debbabi, and Benjamin C. M. Fung. "Authorship Characterization." In Machine Learning for Authorship Attribution and Cyber Forensics, 89–94. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61675-5_7.

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Iqbal, Farkhund, Mourad Debbabi, and Benjamin C. M. Fung. "Authorship Verification." In Machine Learning for Authorship Attribution and Cyber Forensics, 95–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61675-5_8.

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Fang, Alex Chengyu, and Jing Cao. "Semantic Features and Authorship Attribution." In Text Genres and Registers: The Computation of Linguistic Features, 167–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45100-7_10.

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Hajja, Maha, Ahmad Yahya, and Adnan Yahya. "Authorship Attribution of Arabic Articles." In Communications in Computer and Information Science, 194–208. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32959-4_14.

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Conference papers on the topic "Authorship attribution"

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Bozkurt, Ilker Nadi, Ozgur Baghoglu, and Erkan Uyar. "Authorship attribution." In 2007 22nd International Symposium on Computer and Information Sciences - ISCIS '07. IEEE, 2007. http://dx.doi.org/10.1109/iscis.2007.4456854.

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Stamatatos, E., N. Fakotakis, and G. Kokkinakis. "Automatic authorship attribution." In the ninth conference. Morristown, NJ, USA: Association for Computational Linguistics, 1999. http://dx.doi.org/10.3115/977035.977057.

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Pinho, Armando J., Diogo Pratas, and Paulo J. S. G. Ferreira. "Authorship Attribution Using Relative Compression." In 2016 Data Compression Conference (DCC). IEEE, 2016. http://dx.doi.org/10.1109/dcc.2016.53.

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Stamatatos, Efstathios. "Authorship Attribution Using Text Distortion." In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/e17-1107.

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Rabab'ah, Abdullateef, Mahmoud Al-Ayyoub, Yaser Jararweh, and Monther Aldwairi. "Authorship attribution of Arabic tweets." In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016. http://dx.doi.org/10.1109/aiccsa.2016.7945818.

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Gonzalez, Hugo, Natalia Stakhanova, and Ali A. Ghorbani. "Authorship Attribution of Android Apps." In CODASPY '18: Eighth ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3176258.3176322.

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Savoy, Jacques. "Feature selections for authorship attribution." In the 28th Annual ACM Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2480362.2480541.

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Suman, Chanchal, Sriparna Saha, and Pushpak Bhattacharyya. "Authorship Attribution of Scientific Abstracts." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9956343.

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Ahmed, Al-Falahi, Ramdani Mohamed, Bellafkih Mostafa, and Al-Sarem Mohammed. "Authorship attribution in Arabic poetry." In 2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE, 2015. http://dx.doi.org/10.1109/sita.2015.7358411.

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Zhai, Wanyue, Jonathan Rusert, Zubair Shafiq, and Padmini Srinivasan. "Adversarial Authorship Attribution for Deobfuscation." In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.509.

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