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Outline

Title
Abstract
Introduction
Research Objective
Research Objective 5
Overview of Plagiarism Detection Approaches
Local Similarity Analysis
Global Similarity Analysis
Term Occurrence Analysis
Scope
Similarity vs. Relatedness
Co-Citation Proximity-Based Methods
Design of Citation-Based Detection Algorithms
Database
Conclusion
Summary and Comparison of Test Collections
Evaluation: Plagiarism Case Heun
Conclusion Vroniplag Wiki
Methodology
PMC Oas Collection-Specific Limitations
Results
User Study Limitations
Overview of Retrieval and Ranking Performance
Examples of Confirmed Plagiarism Cases
Conclusion of PMC Oas Evaluation
Conclusion of Evaluations
Conclusion of Evaluations 199
Summary
The Dataset Includes
Future Work
Preliminary PMC Oas Corpus Analysis
References
All Topics
Education
Educational Assessment, Evaluation, and Research

Citation-based Plagiarism Detection

Profile image of Bela GippBela Gipp

2014

https://doi.org/10.1007/978-3-658-06394-8
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This doctoral thesis introduces Citation-based Plagiarism Detection (CbPD), a novel approach to identifying heavily disguised plagiarism in academic texts by leveraging citation patterns as semantic fingerprints. Unlike traditional methods that primarily focus on text similarity, CbPD effectively captures narrative and structural similarities in paraphrased works by analyzing citations, enabling the detection of various forms of disguised plagiarism, which conventional systems often miss. The effectiveness of CbPD is evidenced through evaluations on multiple datasets, including significant real-world cases, demonstrating its superiority in reducing false positives and enhancing detection capabilities when used in conjunction with existing plagiarism detection tools.

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Hybrid System for Plagiarism Detection on A Scientific Paper
Asst. Prof. Dr. Mohammed S. H. Al-Tamimi

2021

Plagiarism Detection Systems are critical in identifying instances of plagiarism, particularly in the educational sector whenever it comes to scientific publications and papers. Plagiarism occurs when any material is copied without the author's consent or attribution. To identify such acts, thorough knowledge of plagiarism types and classes is required. It is feasible to detect several sorts of plagiarism using current tools and methodologies. With the advancement of information and communication technologies (ICT) and the availability of online scientific publications, access to these publications has grown more convenient. Additionally, with the availability of several software text editors, plagiarism detection has become a crucial concern. Numerous scholarly articles have previously examined plagiarism detection and the two most often used datasets for plagiarism detection, WordNet and the PAN Dataset. The researchers described verbatim plagiarism detection as a straightforward case of copying and pasting, and then shed light on clever plagiarism, which is more difficult to detect since it may involve original text alteration, borrowing ideas from other studies, and Other scholars have said that plagiarism can obscure the scientific content by substituting terms, deleting or introducing material, rearranging or changing the original publications. The suggested system incorporated natural language processing (NLP) and machine learning (ML) techniques, as well as an external plagiarism detection strategy based on text mining and similarity analysis. The suggested technique employs a mix of Jaccard and cosine similarity. It was examined using the PAN-PC-11 corpus. The proposed system outperforms previous systems on the PAN-PC-11, as demonstrated by the findings. Additionally, the proposed system obtains an accuracy of 0.96, a recall of 0.86, an F-measure of 0.86, and a PlagDet score of 0.86. (0.86). 0.865 and the proposed technique is substantiated by a design application that is used to detect plagiarism in scientific publications and generate nonmedication notifications. Portable Document Format (PDF) .

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Citation pattern matching algorithms for citation-based plagiarism detection: greedy citation tiling, citation chunking and longest common citation sequence
Bela Gipp

Proceedings of the 11th Acm Symposium on Document Engineering, 2011

Plagiarism Detection Systems have been developed to locate instances of plagiarism e.g. within scientific papers. Studies have shown that the existing approaches deliver reasonable results in identifying copy&paste plagiarism, but fail to detect more sophisticated forms such as paraphrased, translated or idea plagiarism. The authors of this paper demonstrated in recent studies that the detection rate can be significantly improved by not only relying on text analysis, but by additionally analyzing the citations of a document. Citations are valuable language independent markers that are similar to a fingerprint. In fact, our examinations of real world cases have shown that the order of citations in a document often remains similar even if the text has been strongly paraphrased or translated in order to disguise plagiarism. This paper introduces three algorithms and discusses their suitability for the purpose of Citation-based Plagiarism Detection. Due to the numerous ways in which plagiarism can occur, these algorithms need to be versatile. They must be capable of detecting transpositions, scaling and combinations in a local and global form. The algorithms are coined Greedy Citation Tiling, Citation Chunking and Longest Common Citation Sequence. The evaluation showed that common forms of plagiarism can be detected reliably if these algorithms are combined.

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

Journal of the American Society for Information Science and Technology, 2012

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,

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Comparing and combining Content- and Citation-based approaches for plagiarism detection
Solange Pertile

2016

The vast amount of scientific publications available online makes it easier for students and researchers to reuse text from other authors and makes it harder for checking the originality of a given...

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Plagiarism Detection Methods and Tools: An Overview
Asst. Prof. Dr. Mohammed S. H. Al-Tamimi

2021

Plagiarism Detection Systems play an important role in revealing instances of a plagiarism act, especially in the educational sector with scientific documents and papers. The idea of plagiarism is that when any content is copied without permission or citation from the author. To detect such activities, it is necessary to have extensive information about plagiarism forms and classes. Thanks to the developed tools and methods it is possible to reveal many types of plagiarism. The development of the Information and Communication Technologies (ICT) and the availability of the online scientific documents lead to the ease of access to these documents. With the availability of many software text editors, plagiarism detections becomes a critical issue. A large number of scientific papers have already investigated in plagiarism detection, and common types of plagiarism detection datasets are being used for recognition systems, WordNet and PAN Datasets have been used since 2009. The researchers have defined the operation of verbatim plagiarism detection as a simple type of copy and paste. Then they have shed the lights on intelligent plagiarism where this process became more difficult to reveal because it may include manipulation of original text, adoption of other researchers' ideas, and translation to other languages, which will be more challenging to handle. Other researchers have expressed that the ways of plagiarism may overshadow the scientific text by replacing, removing, or inserting words, along with shuffling or modifying the original papers. This paper gives an overall definition of plagiarism and works through different papers for the most known types of plagiarism methods and tools.

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Academic Plagiarism Detection
Norman Meuschke

ACM Computing Surveys

This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further.

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HyPlag: A Hybrid Approach to Academic Plagiarism Detection
Norman Meuschke, Vincent Stange

Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018

Current plagiarism detection systems reliably find instances of copied and moderately altered text, but often fail to detect strong paraphrases, translations, and the reuse of non-textual content and ideas. To improve upon the detection capabilities for such concealed content reuse in academic publications, we make four contributions: i) We present the first plagiarism detection approach that combines the analysis of mathematical expressions, images, citations and text. ii) We describe the implementation of this hybrid detection approach in the research prototype HyPlag. iii) We present novel visualization and interaction concepts to aid users in reviewing content similarities identified by the hybrid detection approach. iv) We demonstrate the usefulness of the hybrid detection and result visualization approaches by using HyPlag to analyze a confirmed case of content reuse present in a retracted research publication.

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State-of-the-art in detecting academic plagiarism
Bela Gipp

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 article. Available detection systems use sophisticated and highly efficient character-based text comparisons, which can reliably identify verbatim and moderately disguised copies. Automatically detecting more strongly disguised plagiarism, such as paraphrases, translations or idea plagiarism, is the focus of current research. Proposed approaches for this task include intrinsic, cross-lingual and citation-based plagiarism detection. Each method offers unique strengths and weaknesses; however, none is currently mature enough for practical use. In the future, plagiarism detection systems may benefit from combining traditional character-based detection methods with these emerging detection approaches.

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Computer-based plagiarism detection techniques: A comparative study
Asst. Prof. Dr. Mohammed S. H. Al-Tamimi

2022

Plagiarism is becoming more of a problem in academics. It's made worse by the ease with which a wide range of resources can be found on the internet, as well as the ease with which they can be copied and pasted. It is academic theft since the perpetrator has "taken" and presented the work of others as his or her own. Manual detection of plagiarism by a human being is difficult, imprecise, and time-consuming because it is difficult for anyone to compare their work to current data. Plagiarism is a big problem in higher education, and it can happen on any topic. Plagiarism detection has been studied in many scientific articles, and methods for recognition have been created utilizing the Plagiarism analysis, Authorship identification, and Near-duplicate detection (PAN) Dataset 2009-2011. Verbatim plagiarism, according to the researchers, plagiarism is simply copying and pasting. They then moved on to smart plagiarism, which is more challenging to spot since it might include text change, taking ideas from other academics, and translation into a more difficult-to-manage language. Other studies have found that plagiarism can obscure the scientific content of publications by swapping words, removing or adding material, or reordering or changing the original articles. This article discusses the comparative study of plagiarism detection techniques.

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Review of Recent Plagiarism Detection Techniques and Their Performance Comparison
Ravreet Kaur

Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, 2020

With the explosive growth of technology and the easy availability of content on the web, it creates new challenges to discriminate against the original work from plagiarized material. Content is said to be plagiarized when it is taken from other original sources without giving its reference. To address this issue Plagiarism detection tools are required. Over the years, extensive work has been done in the development of anti-plagiarism tools. This paper presents the types of plagiarism with an aim to review Extrinsic Plagiarism detection techniques using Linguistic-based features, Syntactic-based features, and Semantic-based features. Further, an overview of some current state of art methodologies and their results has been discussed on the dataset of PAN-PC 2009, PAN-PC 2010, and PAN-PC 2011. This paper also analyzes the pros and cons of some existing systems and by comparing results it also identifies that some of the systems have less potency to detect the manual and highly shuffled complex types of plagiarism such as translation obfuscation. Keywords Plagiarism detection • Extrinsic plagiarism detection • Intrinsic plagiarism detection • PAN-PC datasets 1 Introduction World Wide Web provides access to data present in the documents, databases, and other sources of information using internet service. The availability of knowledge and information in the digital form leads to "Plagiarism" by "Plagiarist". Plagiarism

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