Abstract
Authorship attribution is the challenging and promising research field of digital forensics. It determines the plausible author of a text message written by an author by investigating other documents written by that author. Analysis of online messages is helpful to examine the text content in order to draw conclusion about attribution of authorship. Forensics analysis of online messages involves analyzing long fraud documents, terrorists secret communication, suicide letters, threatening mails, emails, blog posts, and also short texts such as SMS text messages, Twitter streams, or Facebook status updates to check the authenticity and identify fraudulence. This paper evaluates the performance of various classifiers for authorship attribution of online messages using proposed wordprint approach. Data mining classification techniques selected for performing the task of authorship attribution are SVM, K-NN, and naive Bayes. Also, performance analysis of frequent words was evaluated using same experimental setup.
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