Local Histograms of Character N-grams for Authorship Attribution

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 288–298, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Local Histograms of Character N-grams for Authorship Attribution Hugo Jair Escalante Graduate Program in Systems Eng. Universidad Aut´onoma de Nuevo Le´on, San Nicol´as de los Garza, NL, 66450, M´exico hugo.jair@gmail.com Thamar Solorio Dept. of Computer and Information Sciences University of Alabama at Birmingham, Birmingham, AL, 35294, USA solorio@cis.uab.edu Manuel Montes-y-G´omez Computer Science Department, INAOE, Tonantzintla, Puebla, 72840, M´exico Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL, 35294, USA mmontesg@cis.uab.edu Abstract This paper proposes the use of local his- tograms (LH) over character n-grams for au- thorship attribution (AA). LHs are enriched histogram representations that preserve se- quential information in documents; they have been successfully used for text categorization and document visualization using word his- tograms. In this work we explore the suitabil- ity of LHs over n-grams at the character-level for AA. We show that LHs are particularly helpful for AA, because they provide useful information for uncovering, to some extent, the writing style of authors. We report experi- mental results in AA data sets that confirm that LHs over character n-grams are more help- ful for AA than the usual global histograms, yielding results far superior to state of the art approaches. We found that LHs are even more advantageous in challenging conditions, such as having imbalanced and small training sets. Our results motivate further research on the use of LHs for modeling the writing style of authors for related tasks, such as authorship verification and plagiarism detection. 1 Introduction Authorship attribution (AA) is the task of deciding whom, from a set of candidates, is the author of a given document (Houvardas and Stamatatos, 2006; Luyckx and Daelemans, 2010; Stamatatos, 2009b). There is a broad field of application for AA meth- ods, including spam filtering (de Vel et al., 2001), fraud detection, computer forensics (Lambers and Veenman, 2009), cyber bullying (Pillay and Solorio, 2010) and plagiarism detection (Stamatatos, 2009a). Therefore, the development of automated AA tech- niques has received much attention recently (Sta- matatos, 2009b). The AA problem can be natu- rally posed as one of single-label multiclass clas- sification, with as many classes as candidate au- thors. However, unlike usual text categorization tasks, where the core problem is modeling the the- matic content of documents (Sebastiani, 2002), the goal in AA is modeling authors’ writing style (Sta- matatos, 2009b). Hence, document representations that reveal information about writing style are re- quired to achieve good accuracy in AA. Word and character based representations have been used in AA with some success so far (Houvar- das and Stamatatos, 2006; Luyckx and Daelemans, 2010; Plakias and Stamatatos, 2008b). Such rep- resentations can capture style information through word or character usage, but they lack sequential in- formation, which can reveal further stylistic infor- mation. In this paper, we study the use of richer document representations for the AA task. In partic- ular, we …

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