An Analysis Framework for Hybrid Authorship Verification

Given a set of candidate authors for whom some texts of undisputed authorship exist, attribute texts of unknown authorship to one of the candidates is called Author verification. This problem acquired great attention due to its new applications in forensic analysis, e-commerce and plagiarism detection. The author verification task is of great help in the plagiarism detection process. Indeed, the probability of plagiarism increases where two parts of a document are not assigned to the same author. This paper introduces an analysis framework for hybrid authorship verification. In fact, the proposed method takes advantage of a large set of linguistic features to fully address the identification of the document’s author. These features are explored to build a machine-learning process. We obtained promising results by relying on PAN@CLEF 2014 English literature corpus.