The purpose of this survey was to investigate journal editors’ use of CrossCheck, powered by iThenticate, to detect plagiarism, and their attitude to potential plagiarism once discovered. A 22-question survey was sent to 3,305 recipients, primarily scholarly journal editors from Anglophone countries, and a reduced 10-question version to 607 editors from non-Anglophone countries. The response ra...

Plagiarism detection systems comprise various approaches that aim to create a fair environment for academic publications and appropriately acknowledge the authors’ works. While the need for a reliable and performant plagiarism detection system increases with an increasing amount of publications, current systems still have shortcomings. Particularly intelligent research plagiarism detection stil...

Plagiarism is a problem in many education institutions around the world. Preventing digital plagiarism requires enormous amount of work from educator. In this paper we concentrate on implementation of well known anti-plagiarism algorithm for local and global search for the original source of plagiarized assignment. We first discuss problems in the implementation of existing anti-plagiarism syst...

The problem of academic plagiarism has been present for centuries. Yet, the widespread dissemination of information technology, including the internet, made plagiarising much easier. Consequently, methods and systems aiding in the detection of plagiarism have attracted much research within the last two decades. Researchers proposed a variety of solutions, which we will review comprehensively in...

This paper describes how a Persian text alignment corpus was constructed to evaluate plagiarism detection systems. This corpus is in PAN format and contains 11,089 documents and more than 11,603 plagiarism cases. Efforts were made to simulate various types of plagiarism manually, semi-automatically, or automatically in this large-scale corpus.

We present an evaluation framework for plagiarism detection.1 The framework provides performance measures that address the specifics of plagiarism detection, and the PAN-PC-10 corpus, which contains 64 558 artificial and 4 000 simulated plagiarism cases, the latter generated via Amazon’s Mechanical Turk. We discuss the construction principles behind the measures and the corpus, and we compare t...

Stevenson, 2004) Key Parts The opportunity to design and deliver short programs on referencing and avoiding plagiarism for transnational UniSA students has confirmed the necessity of combating both the ‘all-plagiarism-is-cheating’ reaction and the ‘just-give-them-a-referencing-guide’ response. The notion of referencing is but the tip of a particularly large and intricate iceberg. Consequently, ...

This work presents results of the ongoing novel research in the area of natural language processing focusing on plagiarism detection, semantic networks and semantic compression. The results demonstrate that the semantic compression is a valuable addition to the existing methods used in plagiary detection. The application of the semantic compression boosts the efficiency of Sentence Hashing Algo...