Intrinsic plagiarism analysis

Abstract

Research in automatic text plagiarism detection focuses on algorithms that compare suspicious documents against a collection of reference documents. Recent approaches perform well in identifying copied or modified foreign sections, but they assume a closed world where a reference collection is given. This article investigates the question whether plagiarism can be detected by a computer program if no reference can be provided, e.g., if the foreign sections stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism analysis; it is closely related to the problem of authorship verification. Our contributions are threefold. (1) We organize the algorithmic building blocks for intrinsic plagiarism analysis and authorship verification and survey the state of the art.(2) We show how the meta learning approach of Koppel and Schler, termed ''unmasking'', can be employed to post-process unreliable stylometric analysis results. (3) We operationalize and evaluate an analysis chain that combines document chunking, style model computation, one-class classification, and meta learning. Problem statementIn the following, the term plagiarism refers to text plagiarism, i.e., the use of another author's information, language, or writing, when done without proper acknowledgment of the original source. Plagiarism detection refers to the unveiling of text plagiarism. Existing approaches to computer-based plagiarism detection break down this task into manageable parts:''Given a text d and a reference collection D, does d contain a section s for which one can find a document d i [ D that contains a section s i such that under some retrieval model R the similarity u R between s and s i is above a threshold h?''Observe that research on automated plagiarism detection presumes a closed world where a reference collection D is given. Since D can be extremely largepossibly the entire indexed part of the World Wide Web-the main research focus is on efficient search technology: near-similarity search and near-duplicate detection (Brin et al

Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods

“…Many research works have been conducted to tackle the task of intrinsic plagiarism detection [23], [33], [34], [66], [103]- [107]. Fig.…”

Determining Window Size from Plagiarism Corpus for Stylometric Features

“…This is called intrinsic plagiarism detection [9], which in this case can be viewed as a one-class classification problem [2]. The text portion is either classified as written by the same author or classified as not written by the same author and therefore, suspicious.…”

Plagiarism Detection for Indonesian Texts

“…The emergence of IPD is strongly related to Authorship Verification(AV). It can be viewed as a generalization of authorship verification and attribution [9], since the input to the IPD system is a document in isolation, and its task is to find the suspicious sections within that single document [48,165]. Unlike IPD, an authorship verification system is given some pieces of writing examples of an author, for example author X, and its task is to determine whether or not a text is written by X.…”

“…The strategies in IPD approaches typically include the analysis of suspicious document, dplg's writing, that is well known as stylometry analysis. According to Stein et al [165], the appropriate stylometric features for IPD fall into one of the following categories:…”