An effective and scalable framework for authorship attribution query processing

© 2018 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://ieeexplore.ieee.org/document/8457490Authorship attribution aims at identifying the original author of an anonymous text from a given set of candidate authors and has a wide range of applications. The main challenge in authorship attribution problem is that the real-world applications tend to have hundreds of authors, while each author may have a small number of text samples, e.g., 5-10 texts/author. As a result, building a predictive model that can accurately identify the author of an anonymous text is a challenging task. In fact, existing authorship attribution solutions based on long text focus on application scenarios, where the number of candidate authors is limited to 50. These solutions generally report a significant performance reduction as the number of authors increases. To overcome this challenge, we propose a novel data representation model that captures stylistic variations within each document, which transforms the problem of authorship attribution into a similarity search problem. Based on this data representation model, we also propose a similarity query processing technique that can effectively handle outliers. We assess the accuracy of our proposed method against the state-of-the-art authorship attribution methods using real-world data sets extracted from Project Gutenberg. Our data set contains 3000 novels from 500 authors. Experimental results from this paper show that our method significantly outperforms all competitors. Specifically, as for the closed-set and open-set authorship attribution problems, our method have achieved higher than 95% accuracy.This work was supported by the CityU Project under Grant 7200387 and Grant 6000511.Published versio

CEAI: CCM based Email Authorship Identification Model

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2]

CAG : stylometric authorship attribution of multi-author documents using a co-authorship graph

© 2020 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://ieeexplore.ieee.org/document/8962080Stylometry has been successfully applied to perform authorship identification of single-author documents (AISD). The AISD task is concerned with identifying the original author of an anonymous document from a group of candidate authors. However, AISD techniques are not applicable to the authorship identification of multi-author documents (AIMD). Unlike AISD, where each document is written by one single author, AIMD focuses on handling multi-author documents. Due to the combinatoric nature of documents, AIMD lacks the ground truth information - that is, information on writing and non-writing authors in a multi-author document - which makes this problem more challenging to solve. Previous AIMD solutions have a number of limitations: (i) the best stylometry-based AIMD solution has a low accuracy, less than 30%; (ii) increasing the number of co-authors of papers adversely affects the performance of AIMD solutions; and (iii) AIMD solutions were not designed to handle the non-writing authors (NWAs). However, NWAs exist in real-world cases - that is, there are papers for which not every co-author listed has contributed as a writer. This paper proposes an AIMD framework called the Co-Authorship Graph that can be used to (i) capture the stylistic information of each author in a corpus of multi-author documents and (ii) make a multi-label prediction for a multi-author query document. We conducted extensive experimental studies on one synthetic and three real-world corpora. Experimental results show that our proposed framework (i) significantly outperformed competitive techniques; (ii) can effectively handle a larger number of co-authors in comparison with competitive techniques; and (iii) can effectively handle NWAs in multi-author documents.This work was supported in part by the Digital Economy Promotion Agency under Project MP-62-0003, and in part by the Thailand Research Fund and Office of the Higher Education Commission under Grant MRG6180266.Published versio

Atribuição de autoria em micro-mensagens

Orientadores: Ariadne Maria Brito Rizzoni Carvalho, Anderson de Rezende RochaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: Com o crescimento continuo do uso de midias sociais, a atribuição de autoria tem um papel imortante na prevenção dos crimes cibernéticos e na análise de rastros online deixados por assediadores, \textit{bullies}, ladrões de identidade entre outros. Nesta dissertação, nós propusemos um método para atribuição de autoria que é de cem a mil vezes mais rápido que o estado da arte. Nós também obtivemos uma acurácia 65\% na classificação de 50 autores. O método proposto se baseia numa representação de caracteristicas escalável utilizando os padrões das mensagens dos micro-blogs, e também nos utilizamos de um classificador de padrões customizado para lidar com grandes quantidades de dados e alta dimensionalidade. Por fim, nós discutimos a redução do espaço de busca na análise de centenas de suspeitos online e milões de micro mensagens online, o que torna essa abordagem valiosa para forense digital e aplicação das leisAbstract: With the ever-growing use of social media, authorship attribution plays an important role in avoiding cybercrime, and helping the analysis of online trails left behind by cyber pranks, stalkers, bullies, identity thieves and alike. In this dissertation, we propose a method for authorship attribution in micro blogs with efficiency one hundred to a thousand times faster than state-of-the-art counterparts. We also achieved a accuracy of 65% when classifying texts from 50 authors. The method relies on a powerful and scalable feature representation approach taking advantage of user patterns on micro-blog messages, and also on a custom-tailored pattern classifier adapted to deal with big data and high-dimensional data. Finally, we discuss search space reduction when analysing hundreds of online suspects and millions of online micro messages, which makes this approach invaluable for digital forensics and law enforcementMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Two-layer classification and distinguished representations of users and documents for grouping and authorship identification

Most studies on authorship identification reported a drop in the identification result when the number of authors exceeds 20-25. In this paper, we introduce a new user representation to address this problem and split classification across two layers. There are at least 3 novelties in this paper. First, the two-layer approach allows applying authorship identification over larger number of authors (tested over 100 authors), and it is extendable. The authors are divided into groups that contain smaller number of authors. Given an anonymous document, the primary layer detects the group to which the document belongs. Then, the secondary layer determines the particular author inside the selected group. In order to extract the groups linking similar authors, clustering is applied over users rather than documents. Hence, the second novelty of this paper is introducing a new user representation that is different from document representation. Without the proposed user representation, the clustering over documents will result in documents of author(s) distributed over several clusters, instead of a single cluster membership for each author. Third, the extracted clusters are descriptive and meaningful of their users as the dimensions have psychological backgrounds. For authorship identification, the documents are labelled with the extracted groups and fed into machine learning to build classification models that predicts the group and author of a given document. The results show that the documents are highly correlated with the extracted corresponding groups, and the proposed model can be accurately trained to determine the group and the author identity

A scalable framework for stylometric analysis query processing

This is an accepted manuscript of an article published by IEEE in 2016 IEEE 16th International Conference on Data Mining (ICDM) on 02/02/2017, available online: https://ieeexplore.ieee.org/document/7837960 The accepted version of the publication may differ from the final published version.Stylometry is the statistical analyses of variationsin the author's literary style. The technique has been used inmany linguistic analysis applications, such as, author profiling, authorship identification, and authorship verification. Over thepast two decades, authorship identification has been extensivelystudied by researchers in the area of natural language processing. However, these studies are generally limited to (i) a small number of candidate authors, and (ii) documents with similar lengths. In this paper, we propose a novel solution by modeling authorship attribution as a set similarity problem to overcome the two stated limitations. We conducted extensive experimental studies on a real dataset collected from an online book archive, Project Gutenberg. Experimental results show that in comparison to existing stylometry studies, our proposed solution can handlea larger number of documents of different lengths written by alarger pool of candidate authors with a high accuracy.Published versio