Using structural information and citation evidence to detect significant plagiarism cases in scientific publications

Abstract

In plagiarism detection (PD) systems, two important problems should be considered: the problem of retrieving candidate documents that are globally similar to a document q under investigation, and the problem of side-by-side comparison of q and its candidates to pinpoint plagiarized fragments in detail. In this article, the authors investigate the usage of structural information of scientific publications in both problems, and the consideration of citation evidence in the second problem. Three statistical measures namely Inverse Generic Class Frequency, Spread, and Depth are introduced to assign a degree of importance (i.e., weight) to structural components in scientific articles. A term-weighting scheme is adjusted to incorporate component-weight factors, which is used to improve the retrieval of potential sources of plagiarism. A plagiarism screening process is applied based on a measure of resemblance, in which component-weight factors are exploited to ignore less or nonsignificant plagiarism cases. Using the notion of citation evidence, parts with proper citation evidence are excluded, and remaining cases are suspected and used to calculate the similarity index. The authors compare their approach to two flat-based baselines, TF-IDF weighting with a Cosine coefficient, and shingling with a Jaccard coefficient. In both baselines, they use different comparison units with overlapping measures for plagiarism screening. They conducted extensive experiments using a dataset of 15,412 documents divided into 8,657 source publications and 6,755 suspicious queries, which included 18,147 plagiarism cases inserted automatically. Component-weight factors are assessed using precision, recall, and F -measure averaged over a 10-fold cross-validation and compared using the ANOVA statistical test. Results from structural-based candidate retrieval and plagiarism detection are evaluated statistically against the flat baselines using paired-t tests on 10-fold cross-validation runs, which demonstrate the efficacy achieved by the proposed framework. An empirical study on the system's response shows that structural information, unlike existing plagiarism detectors, helps to flag significant plagiarism cases, improve the similarity index, and provide human-like plagiarism screening results.

iPlag: Intelligent Plagiarism Reasoner in scientific publications

“…To detect translated plagiarism, a number of cross-language plagiarism detection approaches have been proposed [33][34][35][36]. A recent work [37] has discussed the use of structural information in scientific publications to detect significant plagiarism cases which, in fact, contributes to the concept of idea plagiarism (see Figure 1; Idea Adoption). This work is a complement and supplement to the work in [37].…”

“…A recent work [37] has discussed the use of structural information in scientific publications to detect significant plagiarism cases which, in fact, contributes to the concept of idea plagiarism (see Figure 1; Idea Adoption). This work is a complement and supplement to the work in [37]. Here we propose a framework using a more detailed representation of documents and a different plagiarism screening methods.…”

“…In [37], we explored the use of structural information to detect significant plagiarism cases in scientific publications. Different functions (eleven equations) were proposed to compute components' weights based on three statistical measures namely Inverse Generic Class Frequency, Depth and Spread.…”

“…Different functions (eleven equations) were proposed to compute components' weights based on three statistical measures namely Inverse Generic Class Frequency, Depth and Spread. The study compared these functions and reported that combined Spread and Depth improved the retrieval of potential candidates to the suspected document and yielded better detection results [37].…”

“…Similarly in [37], we modified the term frequencyinverse document frequency (TF-IDF) weighting used in typical information retrieval systems to comprise the weights of structural components. Thus, the similarity formula of a document d with regard to a user query q is given by:…”