The similarity index of scientific publications with equations and formulas, identification of self-plagiarism, and testing of the iThenticate system

TL;DR

This paper examines the challenges of calculating similarity indices in scientific texts with equations, evaluates iThenticate's effectiveness in detecting plagiarism involving formulas, and proposes improvements for software systems in this domain.

Contribution

It introduces the first analysis of similarity index estimation in publications with equations and tests iThenticate's capabilities, highlighting its limitations and suggesting enhancements.

Findings

Equations significantly complicate similarity analysis.

iThenticate often confuses self-plagiarism with pseudo-self-plagiarism.

Proposed methods aim to improve software detection accuracy.

Abstract

The problems of estimating the similarity index of mathematical and other scientific publications containing equations and formulas are discussed for the first time. It is shown that the presence of equations and formulas (as well as figures, drawings, and tables) is a complicating factor that significantly complicates the study of such texts. It is shown that the method for determining the similarity index of publications, based on taking into account individual mathematical symbols and parts of equations and formulas, is ineffective and can lead to erroneous and even completely absurd conclusions. The possibilities of the most popular software system iThenticate, currently used in scientific journals, are investigated for detecting plagiarism and self-plagiarism. The results of processing by the iThenticate system of specific examples and special test problems containing equations (PDEs and ODEs), exact solutions, and some formulas are presented. It has been established that this software system when analyzing inhomogeneous texts, is often unable to distinguish self-plagiarism from pseudo-self-plagiarism (false self-plagiarism). A model complex situation is considered, in which the identification of self-plagiarism requires the involvement of highly qualified specialists of a narrow profile. Various ways to improve the work of software systems for comparing inhomogeneous texts are proposed. This article will be useful to researchers and university teachers in mathematics, physics, and engineering sciences, programmers dealing with problems in image recognition and research topics of digital image processing, as well as a wide range of readers who are interested in issues of plagiarism and self-plagiarism.