Plagiarism detection

About: Plagiarism detection is a research topic. Over the lifetime, 1790 publications have been published within this topic receiving 24740 citations.

Deterring Plagiarism: A New Role for Librarians

Abstract: Introduction The proliferation of student plagiarism on university campuses is paralleled by the increasing number of articles appearing in academic journals presenting varying opinions on the topic. Opinions run the gamut from outrage at the student offenders to pointing fingers at faculty members who fail to create plagiarism-proof assignments. One also reads about controversial new methods for deterring and detecting plagiarism, most notably, online plagiarism detection systems. In surveying the literature, one can construct valid arguments for each point of view. This paper will explore the plagiarism dilemma from a librarian's vantage point, and will outline the strong support that has been offered to teaching faculty with plagiarism problems by the Joan and Donald E. Axinn Library of Hofstra University. It will also examine how Hofstra University decided to subscribe to Turnitin.com (www.turnitin.com), a popular but controversial online plagiarism detection system. As librarians, we know that detection is not the main objective in a campaign against plagiarism. Rather, universities should concentrate on educating students as to what constitutes plagiarism and how to avoid it. Consequently, as our last point we will summarize how Hofstra librarians are reaching out to both faculty and students in order to inform them about this fundamental concern. This paper will not necessarily offer the definitive philosophical answer to solving the plagiarism dilemma, but will attempt to convey a "reality" account of how we have dealt with student plagiarism at Hofstra University. Overview of the Plagiarism Problem Hofstra University is a mid-sized liberal arts university on Long Island with approximately 10,000 full- and part-time undergraduate students and about 3,700 graduate students. In addition, the Hofstra University School of Law has an enrollment of 1,700. In recent years, Hofstra, like other universities, has watched as students became adept at cutting and pasting from the Web, or purchasing papers from paper mills. Part of the dilemma is that many students are unfamiliar with what determines plagiarism and they stumble into it unawares, not only because they have never learned how to use sources, but sometimes because they have been taught that research means plagiarism (White 205). This sense of vagueness is exacerbated by the fact that, with the advent of the Internet, students have unlimited access to information. Additionally, the need for high GPAs to gain entrance to prestigious graduate schools creates an atmosphere of "anything goes" when it comes to completing research assignments. Even a school such as the University of Virginia, long noted for its honor system, has fallen victim to cheating scandals. When confronted with the possibility that some of his students might have plagiarized, Professor Louis Bloomfield of UVA devised a computer program that detected students who had used "recycled" papers from his previous classes. He discovered that 158 of the 500 students in his Physics 105-106 class had cheated (Cullen 2002). This discouraging incident highlights the extent of the plagiarism problem and it also underscores the fact that students' thirst for knowledge has been replaced by a quest for good grades. The problem is so huge that the popular media is now focusing attention to it. The CBS television news program 60 Minutes devoted a segment to cheaters and Professor Donald L. McCabe, founder of the Center for Academic Integrity (www.academicintegrity.org/), told Morley Safer that pressure has turned competitive schools like UVA into academic rat races. In addition to academic pressure, there is the general slackening of ethical codes in society that seems to give the students the go-ahead to succeed at any cost. Students hear of noted historians who have plagiarized, corporate accountants who have cooked the books, and alleged plagiarized material from the Internet being presented recently at a critical United Nations session on Iraq; sadly, they see no harm in a little cheating on their part. …

A Review on Plagiarism Detection Tools

Abstract: Plagiarism has become an increasingly serious problem in the academic world. It is aggravated by the easy access to and the ease of cutting and pasting from a wide range of materials available on the internet. It constitutes academic theft the offender has 'stolen' the work of others and presented the stolen work as if it were his or her own. It goes to the integrity and honesty of a person. It stifles creativity and originality, and defeats the purpose of education The plagiarism is a widespread and growing problem in the academic process. The traditional manual detection of plagiarism by human is difficult, not accurate, and time consuming process as it is difficult for any person to verify with the existing data. The main purpose of this paper is to present existing tools about in regards with plagiarism detection. Plagiarism detection tools are useful to the academic community to detect plagiarism of others and avoid such unlawful activity. This paper describes some of the plagiarism detection tools available for plagiarism checking and types of plagiarism.

Semantic Sequence Kin: A Method of Document Copy Detection

Abstract: The string matching and global word frequency model are two basic models of Document Copy Detection, although they are both unsatisfied in some respects. The String Kernel (SK) and Word Sequence Kernel (WSK) may map string pairs into a new feature space directly, in which the data is linearly separable. This idea inspires us with the Semantic Sequence Kin (SSK) and we apply it to document copy detection. SK and WSK only take into account the gap between the first word/term and the last word/term so that it is not good for plagiarism detection. SSK considers each common word’s position information so as to detect plagiarism in a fine granularity. SSK is based on semantic density that is indeed the local word frequency information. We believe these measures diminish the noise of rewording greatly. We test SSK in a small corpus with several common copy types. The result shows that SSK is excellent for detecting non-rewording plagiarism and valid even if documents are reworded to some extent.

Plagiarism detection using Rouge and WordNet

Abstract: With the arrival of digital era and Internet, the lack of information control provides an incentive for people to freely use any content available to them. Plagiarism occurs when users fail to credit the original owner for the content referred to, and such behavior leads to violation of intellectual property. Two main approaches to plagiarism detection are fingerprinting and term occurrence; however, one common weakness shared by both approaches, especially fingerprinting, is the incapability to detect modified text plagiarism. This study proposes adoption of ROUGE and WordNet to plagiarism detection. The former includes ngram co-occurrence statistics, skip-bigram, and longest common subsequence (LCS), while the latter acts as a thesaurus and provides semantic information. N-gram co-occurrence statistics can detect verbatim copy and certain sentence modification, skip-bigram and LCS are immune from text modification such as simple addition or deletion of words, and WordNet may handle the problem of word substitution.