The similarity index of mathematical and other scientific publications with equations and formulas and the problem of self-plagiarism identification

TL;DR

This paper examines the challenges of measuring similarity in scientific texts with equations, critiques existing software tools like iThenticate, and proposes improvements for better detection of self-plagiarism in complex documents.

Contribution

It introduces a new analysis of the limitations of current similarity measures and software in handling texts with equations, and suggests ways to enhance plagiarism detection methods.

Findings

Equations and formulas significantly complicate similarity analysis.

Current software often confuses self-plagiarism with pseudo-self-plagiarism.

Proposed improvements aim to better distinguish genuine self-plagiarism.

Abstract

The problems of estimating the similarity index of inhomogeneous 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 has been proved 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. Possibilities of the most popular software systems Antiplagiat and 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 specific test problems containing equations and formulas are presented. It has been established that this software system, when analyzing heterogeneous texts, is often unable to distinguish self-plagiarism from pseudo-self-plagiarism, seeming real (but false and imaginary) 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 physics, mathematics, and engineering, 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.