Automatic Authorship Attribution

Proceedings of EACL '99 Automatic Authorship Attribution E. Stamatatos, N. Fakotakis and G. Kokkinakis Dept. of Electrical and Computer Engineering University of Patras 26500 - Patras GREECE stamatatos@wcl.ee.upatras.gr Abstract In this paper we present an approach to automatic authorship attribution dealing with real-world (or unrestricted) text. Our method is based on the computational analysis of the input text using a text-processing tool. Besides the style markers relevant to the output of this tool we also use analysis-dependent style markers, that is, measures that represent the way in which the text has been processed. No word frequency counts, nor other lexically-based measures are taken into account. We show that the proposed set of style markers is able to distinguish texts of various authors of a weekly newspaper using multiple regression. All the experiments we present were performed using real-world text downloaded from the World Wide Web. Our approach is easily trainable and fully-automated requiring no manual text preprocessing nor sampling. 1 Introduction The vast majority of the attempts to computer- assisted authorship attribution has been focused on literary texts. In particular, a lot of attention has been paid to the establishment of the authorship of anonymous or doubtful texts. A typical paradigm is the case of the Federalist papers twelve of which are of disputed authorship (Mosteller and Wallace, 1984; Holmes and Forsyth, 1995). Moreover, the lack of a generic and formal definition of the idiosyncratic style of an author has led to the employment of statistical methods (e.g., discriminant analysis, principal components, etc.). Nowadays, the wealth of text available in the World Wide Web in electronic form for a wide variety of genres and languages, as well as the development of reliable text-processing tools open the way for the solution of the authorship attribution problem as regards real-world text. The most important approaches to authorship attribution involve lexically based measures. A lot of style markers have been proposed for measuring the richness of the vocabulary used by the author. For example, the type-token ratio, the hapax legomena (i.e., once-occurring words), the hapax dislegomena (i.e., twice- occurring words), etc. There are also functions that make use of these measures such as Yule's K (Yule, 1944), Honore's R (Honore, 1979), etc. A review of this metrics can be found in (Holmes, 1994). In (Holmes and Forsyth, 1994) five vocabulary richness functions were used in the framework of a multivariate statistical analysis of the Federalist papers and a principal components analysis was performed. All the disputed papers lie in the side of James Madison (rather than Alexander Hamilton) in the space of the first two principal components. However, such measures require the development of large lexicons with specialized information in order to detect the various forms of the lexical units that constitute an author's vocabulary. For languages with a rich morphology, i.e. Modem Greek, this is an important shortcoming. Instead of counting how many words occur certain number of times, Burrows (1987) proposed the use of a set of common function (or context-free) …

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