Jennifer Wiss-Carline

Summary: Plagiarism detection has evolved into a multifaceted research area at the intersection of text analysis, information retrieval, and natural language processing. The challenge stems from the diverse ways in which plagiarists disguise copied content – ranging from verbatim copy-paste to subtle paraphrasing, structural reordering of text, cross-language translation, or even idea plagiarism that copies … Read more

Summary: Plagiarism remains a serious concern in academia and beyond. It not only includes verbatim copy-paste theft of text, but also disguised plagiarism such as paraphrasing, translating content from another language, or stealing ideas without proper credit. Traditional plagiarism detection software relies mainly on text matching and often fails to catch these sophisticated forms (Maurer … Read more

Summary: Plagiarism detection is a critical concern in academia and publishing, and it traditionally relies on text-matching algorithms to find copied or paraphrased passages. However, conventional plagiarism checkers often struggle to detect more covert plagiarism strategies. For example, heavily paraphrased content or translated text can evade detection by simple string matching. As a result, researchers … Read more

Summary: The most promising technical methods of plagiarism detection over the last 10 years combine deep learning, semantic analysis, and hybrid approaches to address increasingly sophisticated forms of plagiarism. 1. Introduction Over the past decade, plagiarism detection has evolved rapidly, driven by advances in machine learning, deep learning, and natural language processing (NLP). Traditional string-matching … Read more

Summary: Plagiarism detection is a crucial task in academia and content creation, traditionally addressed with methods like exact string matching and heuristic rules. However, these rule-based approaches struggle with nuanced or disguised plagiarism, such as heavy paraphrasing. To tackle this challenge, researchers have increasingly turned to machine learning, particularly supervised classification techniques, to automatically learn … Read more

Summary: Plagiarism detection aims to identify instances where an author has copied or closely imitated content from another source without proper attribution. In the digital age, vast amounts of textual data are easily accessible, so plagiarism has become a pressing issue in academia and industry. For instance, a recent survey found that up to 58% … Read more

Summary: Plagiarism is the unacknowledged reuse of someone else’s text, and it remains a serious challenge in academia and publishing. In the digital era, copying and rephrasing text has become easier than ever. So detecting plagiarism effectively is crucial. Traditional plagiarism detection methods typically rely on exact text matching or simple lexical metrics. For example, … Read more

Summary: Academic institutions broadly condemn plagiarism – the act of presenting someone else’s work as one’s own. However, attitudes towards plagiarism vary around the world. Educators often assume that all students share the same understanding of plagiarism. Yet cultural and educational differences influence how plagiarism is perceived and addressed. Understanding these differences is important because … Read more

Summary: Plagiarism detection is the task of identifying instances where content has been inappropriately copied or imitated without proper attribution. Traditional plagiarism detection methods often rely on string matching or fingerprinting techniques to catch verbatim copying. However, plagiarism has evolved beyond simple copy-paste to include paraphrasing, translation, and idea-based plagiarism, among other tactics. These sophisticated … Read more