Authorship attribution based on a probabilistic topic model

Savoy, Jacques

doi:10.1016/j.ipm.2012.06.003

Cited by 60 publications

(27 citation statements)

References 22 publications

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“…Representative solutions are Burger et al [2011], Nirkhi and Dharaskar [2014], and Cavalcante et al [2014]. Exceptions are few recent applications of the topic models that actually combine these two process into one [Pratanwanich and Liò 2014;Savoy 2013a;Seroussi et al 2014]. Still, the two-processes-based studies on authorship analysis problems dominate [Rangel et al 2014].…”

Section: Introductionmentioning

confidence: 99%

Learning Stylometric Representations for Authorship Analysis

Ding

Fung

Iqbal

et al. 2019

IEEE Trans. Cybern.

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Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data. It extracts an author's identity and sociolinguistic characteristics based on the reflected writing styles in the text. It is an essential process for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, most of the previous techniques critically depend on the manual feature engineering process. Consequently, the choice of feature set has been shown to be scenario-or dataset-dependent. In this paper, to mimic the human sentence composition process using a neural network approach, we propose to incorporate different categories of linguistic features into distributed representation of words in order to learn simultaneously the writing style representations based on unlabeled texts for authorship analysis. In particular, the proposed models allow topical, lexical, syntactical, and character-level feature vectors of each document to be extracted as stylometrics. We evaluate the performance of our approach on the problems of authorship characterization and authorship verification with the Twitter, novel, and essay datasets. The experiments suggest that our proposed text representation outperforms the bag-of-lexical-n-grams, Latent Dirichlet Allocation, Latent Semantic Analysis, PVDM, PVDBOW, and word2vec representations.

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Section: Introductionmentioning

confidence: 99%

Learning Stylometric Representations for Authorship Analysis

Ding

Fung

Iqbal

et al. 2019

IEEE Trans. Cybern.

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“…The majority of published works in authorship attribution focus on closed-set attribution where it is assumed that the author of the text under investigation is necessarily a member of a given well-defined set of candidate authors (Stamatatos et al, 2000;Gamon, 2004;Escalante et al, 2011;Schwartz et al, 2013;Savoy, 2013;Seroussi et al, 2014). This setting fits many forensic applications where usually specific individuals have access to certain resources, have knowledge of certain issues, etc.…”

Section: Introductionmentioning

confidence: 99%

Authorship Attribution Using Text Distortion

Stamatatos¹

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. The proposed method attempts to mask topicspecific information that is not related to the personal style of authors. Based on experiments on two main tasks in authorship attribution, closed-set attribution and authorship verification, we demonstrate that the proposed approach can enhance existing methods especially under cross-topic conditions, where the training and test corpora do not match in topic.

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“…Structural features use the overall organization of the whole text, such as the length or number of sentences and paragraphs. Since lexical features are easy to extract and the result is usually unambiguous, they play the most important role in computational stylometry [17]–[19].…”

Section: Methodsmentioning

confidence: 99%

Automated Authorship Attribution Using Advanced Signal Classification Techniques

et al. 2013

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In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. An open source implementation of our methodology is freely available for use at https://github.com/matthewberryman/author-detection.

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Authorship attribution based on a probabilistic topic model

Cited by 60 publications

References 22 publications

Learning Stylometric Representations for Authorship Analysis

Learning Stylometric Representations for Authorship Analysis

Authorship Attribution Using Text Distortion

Automated Authorship Attribution Using Advanced Signal Classification Techniques

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