Plagiarism detection

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

Authorship Identification using Recurrent Neural Networks

Abstract: Authorship identification is the process of revealing the hidden identity of authors from a corpus of literary data based on a stylometric analysis of the text. It has essential applications in various fields, such as cyber-forensics, plagiarism detection, and political socialization. This paper aims to use a deep learning approach for the task of authorship identification by defining a suitable characterization of texts to capture the distinctive style of an author. The proposed model uses an index based word embedding for the C50 and the BBC datasets, applied to the input data of article level Long Short Term Memory (LSTM) network and Gated Recurrent Unit (GRU) network models. A comparative study of this new variant of embeddings is done with the standard approach of pre-trained word embeddings.

Similarity and Plagiarism in Scholarly Journal Submissions: Bringing Clarity to the Concept for Authors, Reviewers and Editors.

Abstract: What constitutes plagiarism? What are the methods to detect plagiarism? How do “plagiarism detection tools” assist in detecting plagiarism? What is the difference between plagiarism and similarity index? These are probably the most common questions regarding plagiarism that many research experts in scientific writing are usually faced with, but a definitive answer to them is less known to many. According to a report published in 2018, papers retracted for plagiarism have sharply increased over the last two decades, with higher rates in developing and non-English speaking countries.1 Several studies have reported similar findings with Iran, China, India, Japan, Korea, Italy, Romania, Turkey, and France amongst the countries with highest number of retractions due to plagiarism.1,2,3,4 A study reported that duplication of text, figures or tables without appropriate referencing accounted for 41.3% of post-2009 retractions of papers published from India.5 In Pakistan, Journal of Pakistan Medical Association started a special section titled “Learning Research” and published a couple of papers on research writing skills, research integrity and scientific misconduct.6,7 However, the problem has not been adequately addressed and specific issues about it remain unresolved and unclear. According to an unpublished data based on 1,679 students from four universities of Pakistan, 85.5% did not have a clear understanding of the difference between similarity index and plagiarism (unpublished data). Smart et al.8 in their global survey of editors reported that around 63% experienced some plagiarized submissions, with Asian editors experiencing the highest levels of plagiarized/duplicated content. In some papers, journals from non-English speaking countries have specifically discussed the cases of plagiarized submissions to them and have highlighted the drawbacks in relying on similarity checking programs.9,10,11 The cases of plagiarism in non-English speaking countries have a strong message for honest researchers that they should improve their English writing skills and credit used sources by properly citing and referencing them.12 Despite aggregating literature on plagiarism from non-Anglophonic countries, the answers to the aforementioned questions remain unclear. In order to answer these questions, it is important to have a thorough understanding of plagiarism and bring clarity to the less known issues about it. Therefore, this paper aims to 1) define plagiarism and growth in its prevalence as well as literature on it; 2) explain the difference between similarity and plagiarism; 3) discuss the role of similarity checking tools in detecting plagiarism and the flaws on completely relying on them; and 4) discuss the phenomenon called Trojan citation. At the end, suggestions are provided for authors and editors from developing countries so that this issue maybe collectively addressed.

Visualizing document similarity using n-grams and latent semantic analysis

Abstract: As the number of information resources and document quantity explodes, efficient tools with intuitive visualization capabilities desperately needed to assist users in conducting document similarity analysis and/or plagiarism detection tasks by discovering hidden relations among documents. This paper proposes a content-based method for document similarity analysis and visualization. The proposed method is based on modeling the relationship between documents and their n-gram phrases, which are generated from the normalized text, exploiting morphology analysis and lexical lookup. Resolving possible morphological ambiguities is carried out by tagging the words within the examined documents. Text indexing and stop-words removal are performed, employing a new technique that is efficient in dealing with multiple long documents. The examined documents' TF-IDF model is constructed using heuristic based pair-wise matching algorithm, considering lexical and syntactic changes. Then, the hidden associations between the documents and their unique n-gram phrases are investigated using Latent Semantic Analysis (LSA). Next, the pairwise document subset and similarity measures are derived from the Singular Value Decomposition (SVD) computations. Different visualization techniques are then applied on the SVD results to expose the hidden relations among the documents under consideration. As Arabic is one of the most morphological and complicated languages, this paper emphasizes Arabic documents similarity analysis and visualization. Various experiments were carried out revealing the strong capabilities of the proposed method in analyzing and visualizing literal and some types of intelligent similarities.

Deterring digital plagiarism, how effective is the digital detection process?

Abstract: Academic dishonesty or plagiarism is a growing problem in today's digital world. Use of plagiarism detection tools can assist faculty to combat this form of academic dishonesty. In this article, a special emphasis is given to text-matching software called SafeAssignmentTM. The advantages and disadvantages of using automated text matching software's are discussed and analyzed in detail. The advantages and disadvantages of using automated text matching software's are discussed and analyzed in detail.