An author-specific-model-based authorship analysis using psycholinguistic aspects and style word patterns

Abstract

Illegal cyber activities can be curbed by means of authorship analysis which intends to identify the authors of a document by scrutinizing the writing style involved in it. One of the major threats associated with online media is the propagation of false statements on behalf of celebrities with the aim of tarnishing their public image especially as a part of online political campaigns. The scenario calls for the need of analyzing the authorship of documents with less contents and capturing the author style from among a large number of candidate authors belonging to the same domain. This is a less explored area of authorship analysis as the task is challenging because traditional methods fail to acquire accuracy when the contents of different authors are pertaining to same topic. Here we propose a method that accomplishes the task of analysis in such an environment, by employing psycholinguistic, lexical, and syntactic aspects of an author combined with word co-occurrences obtained by modeling the style word pattern of the text. The method identifies an author’s individualistic form of expression of emotional aspects, sociolinguistic aspects and word co-occurrences, to obtain an author-style pattern for each candidate author. An author-specific model is generated. The questioned document is fed into the different models so formed, and the final decision regarding the authorship is made based on the ensembled learning method. The experimental results of the proposed method has secured a precision of 0.98 in best case and 0.45 in worst case, thereby illustrating an improvement in the accuracy of authorship attribution of short texts, in comparison with the existing methods.