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Journal articles on the topic 'Authorship attribution'

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Published: 4 June 2021
Last updated: 10 March 2023

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1

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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2

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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3

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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6

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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7

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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8

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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9

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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10

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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11

Koppel, Moshe, Jonathan Schler, and Shlomo Argamon. "Computational methods in authorship attribution." Journal of the American Society for Information Science and Technology 60, no. 1 (January 2009): 9–26. http://dx.doi.org/10.1002/asi.20961.

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12

Jazila, Nur Inda. "CLOSE AND OPEN TASK AUTHORSHIP ATTRIBUTION: A COMPUTATIONAL AUTHORSHIP ANALYSIS." PARADIGM 2, no. 1 (June 29, 2019): 27. http://dx.doi.org/10.18860/prdg.v2i1.6704.

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<p>Authorship analysis is one of the areas lies within forensic linguistics where the main task is to investigate the characteristics of a text in terms of its authorship. Specifically, authorship attribution examines the possibility of an author for having written the text by analyzing the author's other works. This experimental research addresses two problems: which author writes which text (using a closed task authorship attribution) and who writes each text (using an open task of authorship attribution). In doing so, this research uses R to do statistical computing employing both stylo() and classify() functions. Based on carried out experiments with 1-grams as a fixed variable, it is concluded that SVM algorithm may be best used in doing closed task authorship attribution for its 100% consistency, whereas for the open task k-NN algorithm may be best used since it reaches 94% consistency. In addition to open class task, stylo() function may perform better than classify() function since stylo() function provides results closer to the actual answer. As the legal system often challenges authorship analysis for not having a valid methodology, analyzing styles using stylometry and measuring the styles computationally may help forensic linguists to provide an adequate analysis for the legal system. Scientifically this research provides a framework of how to do authorship analysis computationally while practically it is projected can be used as a tool to detect plagiarism.</p><p> </p>
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13

Custódio, José Eleandro, and Ivandré Paraboni. "Stacked authorship attribution of digital texts." Expert Systems with Applications 176 (August 2021): 114866. http://dx.doi.org/10.1016/j.eswa.2021.114866.

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14

Sudheep Elayidom, M., Chinchu Jose, Anitta Puthussery, and Neenu K Sasi. "Text Classification for Authorship Attribution Analysis." Advanced Computing: An International Journal 4, no. 5 (September 30, 2013): 1–10. http://dx.doi.org/10.5121/acij.2013.4501.

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15

Bogdanova, Alina, and Vitaly Romanov. "Explainable source code authorship attribution algorithm." Journal of Physics: Conference Series 2134, no. 1 (December 1, 2021): 012011. http://dx.doi.org/10.1088/1742-6596/2134/1/012011.

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Abstract Source Code Authorship Attribution is a problem that is lately studied more often due improvements in Deep Learning techniques. Among existing solutions, two common issues are inability to add new authors without retraining and lack of interpretability. We address both these problem. In our experiments, we were able to correctly classify 75% of authors for diferent programming languages. Additionally, we applied techniques of explainable AI (XAI) and found that our model seems to pay attention to distinctive features of source code.
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16

Agata, Teru. "Authorship attribution by data compression program." Library and Information Science 54 (March 10, 2006): 1–18. http://dx.doi.org/10.46895/lis.54.1.

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17

Rocha, Anderson, Walter J. Scheirer, Christopher W. Forstall, Thiago Cavalcante, Antonio Theophilo, Bingyu Shen, Ariadne R. B. Carvalho, and Efstathios Stamatatos. "Authorship Attribution for Social Media Forensics." IEEE Transactions on Information Forensics and Security 12, no. 1 (January 2017): 5–33. http://dx.doi.org/10.1109/tifs.2016.2603960.

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18

Juola, Patrick, John Sofko, and Patrick Brennan. "A Prototype for Authorship Attribution Studies." Digital Scholarship in the Humanities 21, no. 2 (April 12, 2006): 169–78. http://dx.doi.org/10.1093/llc/fql019.

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19

Garcia, A. M., and J. C. Martin. "Function Words in Authorship Attribution Studies." Literary and Linguistic Computing 22, no. 1 (November 14, 2006): 49–66. http://dx.doi.org/10.1093/llc/fql048.

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20

Savoy, Jacques. "Authorship Attribution Based on Specific Vocabulary." ACM Transactions on Information Systems 30, no. 2 (May 2012): 1–30. http://dx.doi.org/10.1145/2180868.2180874.

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21

Juola, Patrick. "Large-Scale Experiments in Authorship Attribution." English Studies 93, no. 3 (May 2012): 275–83. http://dx.doi.org/10.1080/0013838x.2012.668792.

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22

Koppel, Moshe, Jonathan Schler, Shlomo Argamon, and Yaron Winter. "The “Fundamental Problem” of Authorship Attribution." English Studies 93, no. 3 (May 2012): 284–91. http://dx.doi.org/10.1080/0013838x.2012.668794.

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23

LAYTON, ROBERT, PAUL WATTERS, and RICHARD DAZELEY. "Recentred local profiles for authorship attribution." Natural Language Engineering 18, no. 3 (June 9, 2011): 293–312. http://dx.doi.org/10.1017/s1351324911000180.

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AbstractAuthorship attribution methods aim to determine the author of a document, by using information gathered from a set of documents with known authors. One method of performing this task is to create profiles containing distinctive features known to be used by each author. In this paper, a new method of creating an author or document profile is presented that detects features considered distinctive, compared to normal language usage. Thisrecentreingapproach creates more accurate profiles than previous methods, as demonstrated empirically using a known corpus of authorship problems. This method, named recentred local profiles, determines authorship accurately using a simple ‘best matching author’ approach to classification, compared to other methods in the literature. The proposed method is shown to be more stable than related methods as parameter values change. Using a weighted voting scheme, recentred local profiles is shown to outperform other methods in authorship attribution, with an overall accuracy of 69.9% on thead-hocauthorship attribution competition corpus, representing a significant improvement over related methods.
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24

Oliveira, W., E. Justino, and L. S. Oliveira. "Comparing compression models for authorship attribution." Forensic Science International 228, no. 1-3 (May 2013): 100–104. http://dx.doi.org/10.1016/j.forsciint.2013.02.025.

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25

Kocher, Mirco, and Jacques Savoy. "Distributed language representation for authorship attribution." Digital Scholarship in the Humanities 33, no. 2 (August 17, 2017): 425–41. http://dx.doi.org/10.1093/llc/fqx046.

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26

Basile, Chiara, Dario Benedetto, Emanuele Caglioti, and Mirko Degli Esposti. "An example of mathematical authorship attribution." Journal of Mathematical Physics 49, no. 12 (December 2008): 125211. http://dx.doi.org/10.1063/1.2996507.

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27

Malyutov, M. B. "Authorship attribution of texts: a review." Electronic Notes in Discrete Mathematics 21 (August 2005): 353–57. http://dx.doi.org/10.1016/j.endm.2005.07.064.

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28

Lichtblau, Daniel, and Catalin Stoean. "Chaos game representation for authorship attribution." Artificial Intelligence 317 (April 2023): 103858. http://dx.doi.org/10.1016/j.artint.2023.103858.

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29

Ivanov, Ivan, Cvetina Hantova, Maria Nisheva, Peter L. Stanchev, and Phillip Ein-Dor. "Software Library for Authorship Identification." Digital Presentation and Preservation of Cultural and Scientific Heritage 5 (September 30, 2015): 91–97. http://dx.doi.org/10.55630/dipp.2015.5.8.

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The aim of this paper is to review some methods for text authorship attribution and to discuss the development of a software library with tools for automatic authorship attribution. The presentation is focused on an analysis of two groups of tools oriented to: (1) methods for extraction of features and (2) methods for computing the distance between character strings based on data compression algorithms.
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30

Nazir, Zulqarnain, Khurram Shahzad, Muhammad Kamran Malik, Waheed Anwar, Imran Sarwar Bajwa, and Khawar Mehmood. "Authorship Attribution for a Resource Poor Language—Urdu." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 3 (May 31, 2022): 1–23. http://dx.doi.org/10.1145/3487061.

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Authorship attribution refers to examining the writing style of authors to determine the likelihood of the original author of a document from a given set of potential authors. Due to the wide range of authorship attribution applications, a plethora of studies have been conducted for various Western, as well as Asian, languages. However, authorship attribution research in the Urdu language has just begun, although Urdu is widely acknowledged as a prominent South Asian language. Furthermore, the existing studies on authorship attribution in Urdu have addressed a considerably easier problem of having less than 20 candidate authors, which is far from the real-world settings. Therefore, the findings from these studies may not be applicable to the real-world settings. To that end, we have made three key contributions: First, we have developed a large authorship attribution corpus for Urdu, which is a low-resource language. The corpus is composed of over 2.6 million tokens and 21,938 news articles by 94 authors, which makes it a closer substitute to the real-world settings. Second, we have analyzed hundreds of stylometry features used in the literature to identify 194 features that are applicable to the Urdu language and developed a taxonomy of these features. Finally, we have performed 66 experiments using two heterogeneous datasets to evaluate the effectiveness of four traditional and three deep learning techniques. The experimental results show the following: (a) Our developed corpus is many folds larger than the existing corpora, and it is more challenging than its counterparts for the authorship attribution task, and (b) Convolutional Neutral Networks is the most effective technique, as it achieved a nearly perfect F1 score of 0.989 for an existing corpus and 0.910 for our newly developed corpus.
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31

Agun, Hayri Volkan, and Ozgur Yilmazel. "Bucketed common vector scaling for authorship attribution in heterogeneous web collections: A scaling approach for authorship attribution." Journal of Information Science 46, no. 5 (July 11, 2019): 683–95. http://dx.doi.org/10.1177/0165551519863350.

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Domain, genre and topic influences on author style adversely affect the performance of authorship attribution (AA) in multi-genre and multi-domain data sets. Although recent approaches to AA tasks focus on suggesting new feature sets and sampling techniques to improve the robustness of a classification system, they do not incorporate domain-specific properties to reduce the negative impact of irrelevant features on AA. This study presents a novel scaling approach, namely, bucketed common vector scaling, to efficiently reduce negative domain influence without reducing the dimensionality of existing features; therefore, this approach is easily transferable and applicable in a classification system. Classification performances on English-language competition data sets consisting of emails and articles and Turkish-language web documents consisting of blogs, articles and tweets indicate that our approach is very competitive to top-performing approaches in English competition data sets and is significantly improving the top classification performance in mixed-domain experiments on blogs, articles and tweets.
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32

Priya, R. Lakshmi, and G. Manimannan. "Authorship Attribution of Tamil Articles of Unknown Authorship Using Neural Network." International Journal of Data Mining And Emerging Technologies 5, no. 2 (2015): 98. http://dx.doi.org/10.5958/2249-3220.2015.00012.9.

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33

Vuckovic-Dekic, Ljiljana. "Authorship-coauthorship." Archive of Oncology 11, no. 3 (2003): 211–12. http://dx.doi.org/10.2298/aoo0303211v.

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The scientific authorship is based on creativity and originality. Apart from being the means for the attribution of the credit, the authorship also comprises the responsibility and accountability, and it is also the basis for evaluation of scientists. Because of steady rise in the number of multiauthored articles in biomedical sciences, the problem of undeserved authorship has emerged. Since the false authorship undermines the very basis of the publication ethics, the scientific community has undertaken measures for the prevention and remedy of such a highly unethical issue.
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34

BJÖRKLUND, JOHANNA, and NIKLAS ZECHNER. "Syntactic methods for topic-independent authorship attribution." Natural Language Engineering 23, no. 5 (August 9, 2017): 789–806. http://dx.doi.org/10.1017/s1351324917000249.

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AbstractThe efficacy of syntactic features for topic-independent authorship attribution is evaluated, taking a feature set of frequencies of words and punctuation marks as baseline. The features are ‘deep’ in the sense that they are derived by parsing the subject texts, in contrast to ‘shallow’ syntactic features for which a part-of-speech analysis is enough. The experiments are made on two corpora of online texts and one corpus of novels written around the year 1900. The classification tasks include classical closed-world authorship attribution, identification of separate texts among the works of one author, and cross-topic authorship attribution. In the first tasks, the feature sets were fairly evenly matched, but for the last task, the syntax-based feature set outperformed the baseline feature set. These results suggest that, compared to lexical features, syntactic features are more robust to changes in topic.
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35

Zhou, Ai, Yijia Zhang, and Mingyu Lu. "Multidimensional Domain Knowledge Framework for Poet Profiling." Electronics 12, no. 3 (January 28, 2023): 656. http://dx.doi.org/10.3390/electronics12030656.

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Authorship profiling is a subtask of authorship identification. This task can be regarded as an analysis of personal writing styles, which has been widely investigated. However, no previous studies have attempted to analyze the authorship of classical Chinese poetry. First, we provide an approach to evaluate the popularity of poets, and we also establish a public corpus containing the top 20 most popular poets in the Tang Dynasty for authorship profiling. Then, a novel poetry authorship profiling framework named multidimensional domain knowledge poet profiling (M-DKPP) is proposed, combining the knowledge of authorship attribution and the text’s stylistic features with domain knowledge described by experts in traditional poetry studies. A case study for Li Bai is used to prove the validity and applicability of our framework. Finally, the performance of M-DKPP framework is evaluated with four poem datasets. On all datasets, the proposed framework outperforms several baseline approaches for authorship attribution.
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36

Ryabko, Boris, and Nadezhda Savina. "Using Data Compression to Build a Method for Statistically Verified Attribution of Literary Texts." Entropy 23, no. 10 (October 3, 2021): 1302. http://dx.doi.org/10.3390/e23101302.

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We consider the problems of the authorship of literary texts in the framework of the quantitative study of literature. This article proposes a methodology for authorship attribution of literary texts based on the use of data compressors. Unlike other methods, the suggested one gives a possibility to make statistically verified results. This method is used to solve two problems of attribution in Russian literature.
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37

Srinivasa, O., N. V., and V. Vijaya. "Authorship Attribution on Imbalanced English Editorial Corpora." International Journal of Computer Applications 169, no. 1 (July 17, 2017): 44–47. http://dx.doi.org/10.5120/ijca2017914587.

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38

Na-Rae Han. "Authorship Attribution in Korean Using Frequency Profiles." Korean Journal of Cognitive Science 20, no. 2 (June 2009): 225–41. http://dx.doi.org/10.19066/cogsci.2009.20.2.006.

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39

Stern, Sacha. "Attribution and Authorship in the Babylonian Talmud." Journal of Jewish Studies 45, no. 1 (April 1, 1994): 28–51. http://dx.doi.org/10.18647/1731/jjs-1994.

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40

Weltzin, Jake F., R. Travis Belote, Leigh T. Williams, Jason K. Keller, and E. Cayenne Engel. "Authorship in ecology: attribution, accountability, and responsibility." Frontiers in Ecology and the Environment 4, no. 8 (October 2006): 435–41. http://dx.doi.org/10.1890/1540-9295(2006)4[435:aieaaa]2.0.co;2.

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41

GURNEY, P. J., and L. W. GURNEY. "Authorship Attribution of the Scriptores Historiae Augustae." Literary and Linguistic Computing 13, no. 3 (September 1, 1998): 119–31. http://dx.doi.org/10.1093/llc/13.3.119.

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42

Grieve, J. "Quantitative Authorship Attribution: An Evaluation of Techniques." Literary and Linguistic Computing 22, no. 3 (May 2, 2007): 251–70. http://dx.doi.org/10.1093/llc/fqm020.

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43

Argamon, S. "Scalability Issues in Authorship Attribution. * Kim Luyckx." Literary and Linguistic Computing 27, no. 1 (December 12, 2011): 95–97. http://dx.doi.org/10.1093/llc/fqr048.

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44

Brand, Amy, Liz Allen, Micah Altman, Marjorie Hlava, and Jo Scott. "Beyond authorship: attribution, contribution, collaboration, and credit." Learned Publishing 28, no. 2 (April 1, 2015): 151–55. http://dx.doi.org/10.1087/20150211.

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45

Varela, P., M. Albonico, E. Justino, and J. Assis. "Authorship Attribution in Latin Languages using Stylometry." IEEE Latin America Transactions 18, no. 04 (April 2020): 729–35. http://dx.doi.org/10.1109/tla.2020.9082216.

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46

Zutshi, Ambika, Gael McDonald, and Linda Kalejs. "Challenges in collaborative writing: addressing authorship attribution." European Business Review 24, no. 1 (January 6, 2012): 28–46. http://dx.doi.org/10.1108/09555341211191535.

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47

Tamboli, Mubin Shoukat, and Rajesh Prasad. "A robust authorship attribution on big period." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 3167. http://dx.doi.org/10.11591/ijece.v9i4.pp3167-3174.

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Authorship attribution is a task to identify the writer of unknown text and categorize it to known writer. Writing style of each author is distinct and can be used for the discrimination. There are different parameters responsible for rectifying such changes. When the writing samples collected for an author when it belongs to small period, it can participate efficiently for identification of unknown sample. In this paper author identification problem considered where writing sample is not available on the same time period. Such evidences collected over long period of time. And character n-gram, word n-gram and pos n-gram features used to build the model. As they are contributing towards style of writer in terms of content as well as statistic characteristic of writing style. We applied support vector machine algorithm for classification. Effective results and outcome came out from the experiments. While discriminating among multiple authors, corpus selection and construction were the most tedious task which was implemented effectively. It is observed that accuracy varied on feature type. Word and character n-gram have shown good accuracy than PoS n-gram.
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48

Mannion, David, and Peter Dixon. "Authorship attribution: the case of Oliver Goldsmith." Journal of the Royal Statistical Society: Series D (The Statistician) 46, no. 1 (March 1997): 1–18. http://dx.doi.org/10.1111/1467-9884.00055.

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49

Rudman, Joseph. "Cherry Picking in Nontraditional Authorship Attribution Studies." CHANCE 16, no. 2 (March 2003): 26–32. http://dx.doi.org/10.1080/09332480.2003.10554845.

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50

Yang, Min, Xiaojun Chen, Wenting Tu, Ziyu Lu, Jia Zhu, and Qiang Qu. "A Topic Drift Model for authorship attribution." Neurocomputing 273 (January 2018): 133–40. http://dx.doi.org/10.1016/j.neucom.2017.08.022.

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