DOCODE 3.0 (DOcument COpy DEtector): A system for plagiarism detection by applying an information fusion process from multiple documental data sources

Abstract

a b s t r a c tPlagiarism refers to the act of presenting external words, thoughts, or ideas as one's own, without providing references to the sources from which they were taken. The exponential growth of different digital document sources available on the Web has facilitated the spread of this practice, making the accurate detection of it a crucial task for educational institutions. In this article, we present DOCODE 3.0, a Web system for educational institutions that performs automatic analysis of large quantities of digital documents in relation to their degree of originality. Since plagiarism is a complex problem, frequently tackled at different levels, our system applies algorithms in order to perform an information fusion process from multi data source to all these levels. These algorithms have been successfully tested in the scientific community in solving tasks like the identification of plagiarized passages and the retrieval of source candidates from the Web, among other multi data sources as digital libraries, and have proven to be very effective. We integrate these algorithms into a multi-tier, robust and scalable JEE architecture, allowing many different types of clients with different requirements to consume our services. For users, DOCODE produces a number of visualizations and reports from the different outputs to let teachers and professors gain insights on the originality of the documents they review, allowing them to discover, understand and handle possible plagiarism cases and making it easier and much faster to analyze a vast number of documents. Our experience here is so far focused on the Chilean situation and the Spanish language, offering solutions to Chilean educational institutions in any of their preferred Virtual Learning Environments. However, DOCODE can easily be adapted to increase language coverage.

Detecting Machine-Obfuscated Plagiarism

“…The research on plagiarism detection has yielded many approaches that employ lexical [4,18], syntactical [33,44], semantic [41,27], or cross-lingual text analysis [12,14]. These approaches reliably detect copied or moderately altered plagiarism.…”

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“…Text Source Code Other Meuschke and Gipp [160] YES NO NO YES NO Chong [47] YES NO NO NO NO Eisa et al [61] YES NO YES NO NO Agarwal and Sharma [8] YES YES NO YES NO Chowdhury et al [48] YES YES NO YES NO Kanjirangat and Gupta [251] YES YES NO YES NO Velasquez et al [256] YES NO NO YES YES Hourrane and Benlahmar [114] YES NO NO NO NO…”

“…Velasquez et al [256] proposed a new plagiarism detection system but also provided an extensive literature review that includes a typology of plagiarism and an overview of six plagiarism detection systems.…”

“…Turnitin LLC is the market leader for plagiarism detection services. The company's plagiarism detection system Turnitin is most frequently cited in papers included in Layer 2 [116,191,256].…”

“…Alfikri and Ayu Purwarianti [13] additionally distinguished as separate forms of academic plagiarism the partial copying of smaller text segments, two forms of paraphrasing that differ regarding whether the sentence structure changes and translations. Velasquez et al [256] distinguished verbatim copying and technical disguise, combined paraphrasing and translation into one form, and categorized the deliberate misuse of references as a separate form. Weber-Wulff [265] and Chowdhury and Bhattacharyya [48] likewise categorized referencing errors as a form of plagiarism.…”