Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods

Plagiarism can be of many different natures, ranging from copying texts to adopting ideas, without giving credit to its originator. This paper presents a new taxonomy of plagiarism that highlights differences between literal plagiarism and intelligent plagiarism, from the plagiarist's behavioral point of view. The taxonomy supports deep understanding of different linguistic patterns in committing plagiarism, for example, changing texts into semantically equivalent but with different words and organization, shortening texts with concept generalization and specification, and adopting ideas and important contributions of others. Different textual features that characterize different plagiarism types are discussed. Systematic frameworks and methods of monolingual, extrinsic, intrinsic, and cross-lingual plagiarism detection are surveyed and correlated with plagiarism types, which are listed in the taxonomy. We conduct extensive study of state-of-the-art techniques for plagiarism detection, including character n-gram-based (CNG), vector-based (VEC), syntax-based (SYN), semantic-based (SEM), fuzzy-based (FUZZY), structural-based (STRUC), stylometric-based (STYLE), and cross-lingual techniques (CROSS). Our study corroborates that existing systems for plagiarism detection focus on copying text but fail to detect intelligent plagiarism when ideas are presented in different words.

I. Introduction

The problem of plagiarism has recently increased because of the digital era of resources available on the World Wide Web. Plagiarism detection in natural languages by statistical or computerized methods has started since the 1990s, which is pioneered by the studies of copy detection mechanisms in digital documents [42], [43]. Earlier than plagiarism detection in natural languages, code clones and software misuse detection has started since the 1970s by the studies to detect programming code plagiarism in Pascal and C [28], [44]–[47]. Algorithms of plagiarism detection in natural languages and programming languages have noticeable differences. The first one tackles different textual features and diverse methods of detection, while the latter mainly focuses on keeping track of metrics, such as number of lines, variables, statements, subprograms, calls to subprograms, and other parameters. During the last decade, research on automated plagiarism detection in natural languages has actively evolved, which takes the advantage of recent developments in related fields like information retrieval (IR), cross-language information retrieval (CLIR), natural language processing, computational linguistics, artificial intelligence, and soft computing. In this paper, a survey of recent advances in the area of automated plagiarism detection in text documents is presented, which started roughly in 2005, unless it is noteworthy to state a research prior than that. Earlier study was excellently reviewed by [48] and [52]–[55].