Content similarity detection

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Plagiarism detection or content similarity detection is the process of locating instances of plagiarism or copyright infringement within a work or document. The widespread use of computers and the advent of the Internet have made it easier to plagiarize the work of others. [1] [2]

Contents

Detection of plagiarism can be undertaken in a variety of ways. Human detection is the most traditional form of identifying plagiarism from written work. This can be a lengthy and time-consuming task for the reader [2] and can also result in inconsistencies in how plagiarism is identified within an organization. [3] Text-matching software (TMS), which is also referred to as "plagiarism detection software" or "anti-plagiarism" software, has become widely available, in the form of both commercially available products as well as open-source[ examples needed ] software. TMS does not actually detect plagiarism per se, but instead finds specific passages of text in one document that match text in another document.

Software-assisted plagiarism detection

Computer-assisted plagiarism detection (CaPD) is an Information retrieval (IR) task supported by specialized IR systems, which is referred to as a plagiarism detection system (PDS) or document similarity detection system. A 2019 systematic literature review [4] presents an overview of state-of-the-art plagiarism detection methods.

In text documents

Systems for text similarity detection implement one of two generic detection approaches, one being external, the other being intrinsic. [5] External detection systems compare a suspicious document with a reference collection, which is a set of documents assumed to be genuine. [6] Based on a chosen document model and predefined similarity criteria, the detection task is to retrieve all documents that contain text that is similar to a degree above a chosen threshold to text in the suspicious document. [7] Intrinsic PDSes solely analyze the text to be evaluated without performing comparisons to external documents. This approach aims to recognize changes in the unique writing style of an author as an indicator for potential plagiarism. [8] [9] PDSes are not capable of reliably identifying plagiarism without human judgment. Similarities and writing style features are computed with the help of predefined document models and might represent false positives. [10] [11] [12] [13] [14]

Effectiveness of those tools in higher education settings

A study was conducted to test the effectiveness of similarity detection software in a higher education setting. One part of the study assigned one group of students to write a paper. These students were first educated about plagiarism and informed that their work was to be run through a content similarity detection system. A second group of students was assigned to write a paper without any information about plagiarism. The researchers expected to find lower rates in group one but found roughly the same rates of plagiarism in both groups. [15]

Approaches

The figure below represents a classification of all detection approaches currently in use for computer-assisted content similarity detection. The approaches are characterized by the type of similarity assessment they undertake: global or local. Global similarity assessment approaches use the characteristics taken from larger parts of the text or the document as a whole to compute similarity, while local methods only examine pre-selected text segments as input.[ citation needed ]

Classification of computer-assisted plagiarism detection methods PDS Classification.png
Classification of computer-assisted plagiarism detection methods
Fingerprinting

Fingerprinting is currently the most widely applied approach to content similarity detection. This method forms representative digests of documents by selecting a set of multiple substrings (n-grams) from them. The sets represent the fingerprints and their elements are called minutiae. [17] [18] A suspicious document is checked for plagiarism by computing its fingerprint and querying minutiae with a precomputed index of fingerprints for all documents of a reference collection. Minutiae matching with those of other documents indicate shared text segments and suggest potential plagiarism if they exceed a chosen similarity threshold. [19] Computational resources and time are limiting factors to fingerprinting, which is why this method typically only compares a subset of minutiae to speed up the computation and allow for checks in very large collection, such as the Internet. [17]

String matching

String matching is a prevalent approach used in computer science. When applied to the problem of plagiarism detection, documents are compared for verbatim text overlaps. Numerous methods have been proposed to tackle this task, of which some have been adapted to external plagiarism detection. Checking a suspicious document in this setting requires the computation and storage of efficiently comparable representations for all documents in the reference collection to compare them pairwise. Generally, suffix document models, such as suffix trees or suffix vectors, have been used for this task. Nonetheless, substring matching remains computationally expensive, which makes it a non-viable solution for checking large collections of documents. [20] [21] [22]

Bag of words

Bag of words analysis represents the adoption of vector space retrieval, a traditional IR concept, to the domain of content similarity detection. Documents are represented as one or multiple vectors, e.g. for different document parts, which are used for pair wise similarity computations. Similarity computation may then rely on the traditional cosine similarity measure, or on more sophisticated similarity measures. [23] [24] [25]

Citation analysis

Citation-based plagiarism detection (CbPD) [26] relies on citation analysis, and is the only approach to plagiarism detection that does not rely on the textual similarity. [27] CbPD examines the citation and reference information in texts to identify similar patterns in the citation sequences. As such, this approach is suitable for scientific texts, or other academic documents that contain citations. Citation analysis to detect plagiarism is a relatively young concept. It has not been adopted by commercial software, but a first prototype of a citation-based plagiarism detection system exists. [28] Similar order and proximity of citations in the examined documents are the main criteria used to compute citation pattern similarities. Citation patterns represent subsequences non-exclusively containing citations shared by the documents compared. [27] [29] Factors, including the absolute number or relative fraction of shared citations in the pattern, as well as the probability that citations co-occur in a document are also considered to quantify the patterns' degree of similarity. [27] [29] [30] [31]

Stylometry

Stylometry subsumes statistical methods for quantifying an author's unique writing style [32] [33] and is mainly used for authorship attribution or intrinsic plagiarism detection. [34] Detecting plagiarism by authorship attribution requires checking whether the writing style of the suspicious document, which is written supposedly by a certain author, matches with that of a corpus of documents written by the same author. Intrinsic plagiarism detection, on the other hand, uncovers plagiarism based on internal evidences in the suspicious document without comparing it with other documents. This is performed by constructing and comparing stylometric models for different text segments of the suspicious document, and passages that are stylistically different from others are marked as potentially plagiarized/infringed. [8] Although they are simple to extract, character n-grams are proven to be among the best stylometric features for intrinsic plagiarism detection. [35]

Neural networks

More recent approaches to assess content similarity using neural networks have achieved significantly greater accuracy, but come at great computational cost. [36] Traditional neural network approaches embed both pieces of content into semantic vector embeddings to calculate their similarity, which is often their cosine similarity. More advanced methods perform end-to-end prediction of similarity or classifications using the Transformer architecture. [37] [38] Paraphrase detection particularly benefits from highly parameterized pre-trained models.

Performance

Comparative evaluations of content similarity detection systems [6] [39] [40] [41] [42] [43] indicate that their performance depends on the type of plagiarism present (see figure). Except for citation pattern analysis, all detection approaches rely on textual similarity. It is therefore symptomatic that detection accuracy decreases the more plagiarism cases are obfuscated.

Detection performance of CaPD approaches depending on the type of plagiarism being present PD Methods Detection Performance.png
Detection performance of CaPD approaches depending on the type of plagiarism being present

Literal copies, a.k.a. copy and paste (c&p) plagiarism or blatant copyright infringement, or modestly disguised plagiarism cases can be detected with high accuracy by current external PDS if the source is accessible to the software. In particular, substring matching procedures achieve good performance for c&p plagiarism, since they commonly use lossless document models, such as suffix trees. The performance of systems using fingerprinting or bag of words analysis in detecting copies depends on the information loss incurred by the document model used. By applying flexible chunking and selection strategies, they are better capable of detecting moderate forms of disguised plagiarism when compared to substring matching procedures.

Intrinsic plagiarism detection using stylometry can overcome the boundaries of textual similarity to some extent by comparing linguistic similarity. Given that the stylistic differences between plagiarized and original segments are significant and can be identified reliably, stylometry can help in identifying disguised and paraphrased plagiarism. Stylometric comparisons are likely to fail in cases where segments are strongly paraphrased to the point where they more closely resemble the personal writing style of the plagiarist or if a text was compiled by multiple authors. The results of the International Competitions on Plagiarism Detection held in 2009, 2010 and 2011, [6] [42] [43] as well as experiments performed by Stein, [34] indicate that stylometric analysis seems to work reliably only for document lengths of several thousand or tens of thousands of words, which limits the applicability of the method to CaPD settings.

An increasing amount of research is performed on methods and systems capable of detecting translated plagiarism. Currently, cross-language plagiarism detection (CLPD) is not viewed as a mature technology [44] and respective systems have not been able to achieve satisfying detection results in practice. [41]

Citation-based plagiarism detection using citation pattern analysis is capable of identifying stronger paraphrases and translations with higher success rates when compared to other detection approaches, because it is independent of textual characteristics. [27] [30] However, since citation-pattern analysis depends on the availability of sufficient citation information, it is limited to academic texts. It remains inferior to text-based approaches in detecting shorter plagiarized passages, which are typical for cases of copy-and-paste or shake-and-paste plagiarism; the latter refers to mixing slightly altered fragments from different sources. [45]

Software

The design of content similarity detection software for use with text documents is characterized by a number of factors: [46]

FactorDescription and alternatives
Scope of searchIn the public internet, using search engines / Institutional databases / Local, system-specific database.[ citation needed ]
Analysis timeDelay between the time a document is submitted and the time when results are made available.[ citation needed ]
Document capacity / Batch processingNumber of documents the system can process per unit of time.[ citation needed ]
Check intensityHow often and for which types of document fragments (paragraphs, sentences, fixed-length word sequences) does the system query external resources, such as search engines.
Comparison algorithm typeThe algorithms that define the way the system uses to compare documents against each other.[ citation needed ]
Precision and recall Number of documents correctly flagged as plagiarized compared to the total number of flagged documents, and to the total number of documents that were actually plagiarized. High precision means that few false positives were found, and high recall means that few false negatives were left undetected.[ citation needed ]

Most large-scale plagiarism detection systems use large, internal databases (in addition to other resources) that grow with each additional document submitted for analysis. However, this feature is considered by some as a violation of student copyright.[ citation needed ]

In source code

Plagiarism in computer source code is also frequent, and requires different tools than those used for text comparisons in document. Significant research has been dedicated to academic source-code plagiarism. [47]

A distinctive aspect of source-code plagiarism is that there are no essay mills, such as can be found in traditional plagiarism. Since most programming assignments expect students to write programs with very specific requirements, it is very difficult to find existing programs that already meet them. Since integrating external code is often harder than writing it from scratch, most plagiarizing students choose to do so from their peers.

According to Roy and Cordy, [48] source-code similarity detection algorithms can be classified as based on either

The previous classification was developed for code refactoring, and not for academic plagiarism detection (an important goal of refactoring is to avoid duplicate code, referred to as code clones in the literature). The above approaches are effective against different levels of similarity; low-level similarity refers to identical text, while high-level similarity can be due to similar specifications. In an academic setting, when all students are expected to code to the same specifications, functionally equivalent code (with high-level similarity) is entirely expected, and only low-level similarity is considered as proof of cheating.

Algorithms

This section is an excerpt from Duplicate code § Detecting duplicate code.[ edit ]

A number of different algorithms have been proposed to detect duplicate code. For example:

Complications with the use of text-matching software for plagiarism detection

Various complications have been documented with the use of text-matching software when used for plagiarism detection. One of the more prevalent concerns documented centers on the issue of intellectual property rights. The basic argument is that materials must be added to a database in order for the TMS to effectively determine a match, but adding users' materials to such a database may infringe on their intellectual property rights. The issue has been raised in a number of court cases.

An additional complication with the use of TMS is that the software finds only precise matches to other text. It does not pick up poorly paraphrased work, for example, or the practice of plagiarizing by use of sufficient word substitutions to elude detection software, which is known as rogeting.

See also

Related Research Articles

Apache Lucene is a free and open-source search engine software library, originally written in Java by Doug Cutting. It is supported by the Apache Software Foundation and is released under the Apache Software License. Lucene is widely used as a standard foundation for production search applications.

A recommender system, or a recommendation system, is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

An annotation is extra information associated with a particular point in a document or other piece of information. It can be a note that includes a comment or explanation. Annotations are sometimes presented in the margin of book pages. For annotations of different digital media, see web annotation and text annotation.

A paraphrase or rephrase is the rendering of the same text in different words without losing the meaning of the text itself. More often than not, a paraphrased text can convey its meaning better than the original words. In other words, it is a copy of the text in meaning, but which is different from the original. For example, when someone tells a story they heard in their own words, they paraphrase, with the meaning being the same. The term itself is derived via Latin paraphrasis, from Ancient Greek παράφρασις (paráphrasis) 'additional manner of expression'. The act of paraphrasing is also called paraphrasis.

Citation analysis is the examination of the frequency, patterns, and graphs of citations in documents. It uses the directed graph of citations — links from one document to another document — to reveal properties of the documents. A typical aim would be to identify the most important documents in a collection. A classic example is that of the citations between academic articles and books. For another example, judges of law support their judgements by referring back to judgements made in earlier cases. An additional example is provided by patents which contain prior art, citation of earlier patents relevant to the current claim. The digitization of patent data and increasing computing power have led to a community of practice that uses these citation data to measure innovation attributes, trace knowledge flows, and map innovation networks.

<span class="mw-page-title-main">Turnitin</span> Internet-based plagiarism-prevention service

Turnitin is an Internet-based similarity detection service run by the American company Turnitin, LLC, a subsidiary of Advance Publications.

Stylometry is the application of the study of linguistic style, usually to written language. It has also been applied successfully to music, paintings, and chess.

Bibliographic coupling, like co-citation, is a similarity measure that uses citation analysis to establish a similarity relationship between documents. Bibliographic coupling occurs when two works reference a common third work in their bibliographies. It is an indication that a probability exists that the two works treat a related subject matter.

<span class="mw-page-title-main">JabRef</span> Reference management software

JabRef is an open-source, cross-platform citation and reference management software. It is used to collect, organize and search bibliographic information.

<span class="mw-page-title-main">Fingerprint (computing)</span> Digital identifier derived from the data by an algorithm

In computer science, a fingerprinting algorithm is a procedure that maps an arbitrarily large data item to a much shorter bit string, its fingerprint, that uniquely identifies the original data for all practical purposes just as human fingerprints uniquely identify people for practical purposes. This fingerprint may be used for data deduplication purposes. This is also referred to as file fingerprinting, data fingerprinting, or structured data fingerprinting.

<span class="mw-page-title-main">Plagiarism</span> Using another authors work as if it was ones own original work

Plagiarism is the representation of another person's language, thoughts, ideas, or expressions as one's own original work. Although precise definitions vary depending on the institution, in many countries and cultures plagiarism is considered a violation of academic integrity and journalistic ethics, as well as social norms around learning, teaching, research, fairness, respect, and responsibility. As such, a person or entity that is determined to have committed plagiarism is often subject to various punishments or sanctions, such as suspension, expulsion from school or work, fines, imprisonment, and other penalties.

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In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. The "topics" produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's balance of topics is.

<span class="mw-page-title-main">Co-citation</span> Frequency with which two documents are cited together by other documents

Co-citation is the frequency with which two documents are cited together by other documents. If at least one other document cites two documents in common, these documents are said to be co-cited. The more co-citations two documents receive, the higher their co-citation strength, and the more likely they are semantically related. Like bibliographic coupling, co-citation is a semantic similarity measure for documents that makes use of citation analyses.

In natural language processing and information retrieval, explicit semantic analysis (ESA) is a vectoral representation of text that uses a document corpus as a knowledge base. Specifically, in ESA, a word is represented as a column vector in the tf–idf matrix of the text corpus and a document is represented as the centroid of the vectors representing its words. Typically, the text corpus is English Wikipedia, though other corpora including the Open Directory Project have been used.

<span class="mw-page-title-main">Co-citation Proximity Analysis</span> Document similarity measure that uses citation analysis

Co-citation Proximity Analysis (CPA) is a document similarity measure that uses citation analysis to assess semantic similarity between documents at both the global document level as well as at individual section-level. The similarity measure builds on the co-citation analysis approach, but differs in that it exploits the information implied in the placement of citations within the full-texts of documents.

<span class="mw-page-title-main">Entity linking</span> Concept in Natural Language Processing

In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris". Entity linking is different from named-entity recognition (NER) in that NER identifies the occurrence of a named entity in text but it does not identify which specific entity it is.

Paraphrase or paraphrasing in computational linguistics is the natural language processing task of detecting and generating paraphrases. Applications of paraphrasing are varied including information retrieval, question answering, text summarization, and plagiarism detection. Paraphrasing is also useful in the evaluation of machine translation, as well as semantic parsing and generation of new samples to expand existing corpora.

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Adversarial stylometry is the practice of altering writing style to reduce the potential for stylometry to discover the author's identity or their characteristics. This task is also known as authorship obfuscation or authorship anonymisation. Stylometry poses a significant privacy challenge in its ability to unmask anonymous authors or to link pseudonyms to an author's other identities, which, for example, creates difficulties for whistleblowers, activists, and hoaxers and fraudsters. The privacy risk is expected to grow as machine learning techniques and text corpora develop.

References

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Literature

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