document: Style mining of electronic messages for multiple authorship discrimination : first results This paper considers the use of computational stylistics for performing authorship attribution of electronic messages , addressing categorization problems with as many as 20 different classes ( authors ) . Effective stylistic characterization of text is potentially useful for a variety of tasks , as language style contains cues regarding the authorship , purpose , and mood of the text , all of which would be useful adjuncts to information retrieval or knowledge-management tasks . We focus here on the problem of determining the author of an anonymous message , based only on the message text . Several multiclass variants of the Winnow algorithm were applied to a vector representation of the message texts to learn models for discriminating different authors . We present results comparing the classification accuracy of the different approaches . The results show that stylistic models can be accurately learned to determine an author 's identity .
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