Plagiarism detection

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

Detection of Common Errors in Turkish EFL Students' Writing through a Corpus Analytic Approach.

Abstract: The present study aims to explore Turkish EFL students’ major writing difficulties by analyzing the frequent writing errors in academic essays. Accordingly, the study examined errors in a corpus of 150 academic essays written by Turkish EFL students studying at the Department of English Language and Literature at a public university in Turkey. The essays were written on assigned topics as take home exam papers or assignments in the context of a first year academic writing course. The corpus consisted of essays of various lengths ranging from 500 word essays to 1500 word essays. The essays were compiled into a corpus and analyzed by using a concordance program. The essays were also checked for plagiarism using the online plagiarism detection software and plagiarized essays were excluded from the analysis. Errors were classified by using an error classification system which was organized according to lexico-grammatical categories. The resulting categories consisted of mostly syntactic and lexical categories of error but academic style errors were considered as well. As a result of the analysis, in terms of error categories, the most frequent errors were observed in the verb related error categories. When considered individually, the most frequent errors were observed in noun modification and were mostly interference related.

Deep Investigation of Cross-Language Plagiarism Detection Methods

Abstract: This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.

A new method for Code Similarity Detection

Abstract: Source code documents are vulnerable to being plagiarized As the central component of Code Plagiarism Detection (CPD), Code Similarity Detection (CSD) attracts more and more attention In this paper, we proposed a new method for CSD by combining structure metric with semantic computing techniques It is capable of identifying not only the primary cheating means in code copy, but also the senior ones, such as replacing control structures with equivalent structures We describe the design and implementation of the method, and make some comparative experiments against MOSS and the structure only method Experiments show that the method proposed in this paper can obtain more effective similar values of code-pairs

AST-based multi-language plagiarism detection method

Abstract: To detect plagiarism on programming course, the plagiarism detection method based on Abstract Syntax Tree (AST) is proposed. First, we parse source codes into the corresponding AST by syntax analyzer and a biology sequence matching algorithm is used to calculate the similarities of programs. Second, the AST features of similar parts of the programs are extracted and then space vectors of the features are obtained. Finally, we find “copy cluster” by clustering the vectors. Experimental results show that this method has a good effect on the detection of plagiarism and can also find the “copy cluster” accurately.