Plagiarism detection

Plagiarism detectionPlagiarism detection in f online assessment proctoring

 

What is Plagiarism?

 

 

Actually, it’s very hard to give an extract definition for the word plagiarism but According to Merriam-Webster dictionary, the simple meaning for plagiarism is “To use the words or ideas of another person as if they were your own words or ideas”. Plagiarism also includes:

1. Turning in someone Else’s work as your own.
2. Copying words or ideas from someone else without giving credit.
3. Failing to put quotations in quotation marks.
4. Giving incorrect information about the source of the quotation.
5. Changing words but copying the sentence structure of a source without giving credit.
6. Copying so many words or ideas from a source that it makes up the majority of your work, whether you give credit or not.

There are two main classes of methods used to reduce plagiarism.

1. Plagiarism Prevention :
   Punishment routines and plagiarism drawback explanation procedures. Require a long time to implement. But have a long-term positive effect.
2. Plagiarism Detection :
   Include manual methods and software tool. Easy to implement, but have a momentary positive effect.

Plagiarism Detection

Plagiarism detection can be done manually or using an automated process. The automated process is very similar to natural language processing, visual identification, and biometric process. All of these have a foundation for pattern recognition. Automated process doesn’t give 100% accuracy. so the manual checking is still needed.

Internal Plagiarism Detection

Finding plagiarized passages within a document without access to potential original text. Also called Intrinsic plagiarism detection.

External Plagiarism Detection

External plagiarism detection consists of comparing suspicious plagiarized documents against potential original documents.

Plagiarism Detection in source code

Detecting Plagiarism in source code is relatively easy than natural language plagiarism detection. Because there is neither ambiguity nor interference between words in programming languages. But in natural language, every word may have many synonyms and different meanings. Some plagiarism detection methods are language independent and some are language-dependent.

Plagiarism Detection in natural language

Detecting plagiarism in written documents. this method can divide into two categories which are called language-independent plagiarism detection and language-dependent plagiarism detection.

Language-Independent Plagiarism Detection

Language independent methods are based on evaluating text characteristics that are common to all languages. Such as the number of special characters and the average length of a sentence. Paraphrasing techniques can be used to mislead the language-independent systems.

Language Dependent Plagiarism Detection

These methods are based on evaluating text characteristics that are specific to one language. Such as counting the frequency of a special word in a particular language. Language dependent plagiarism detection is more effective than language-independent plagiarism detection.

Stylometry — based methods

Stylometry is a statistical approach used for authorship attribution. These are inspired by authorship attribution methods and consist basically of classifying writing styles of authors to identify similarity. It is based on the assumption that every author has a unique style. The writing style can be analyzed by using factors within the same document, or by comparing two documents of the same author. This is performed by dividing the documents into parts like paragraphs and sentences. The style features are then extracted and analyzed. The main linguistic stylometric features are Text statistics which operate at the character level (number of commas, question marks, word lengths, etc).

• Syntactic features to measure writing style at the sentence level (sentence lengths, use of function words, etc.).
• Syntactic features to measure writing style at the sentence level (sentence lengths, use of function words, etc.).
• Closed-class word sets to count special words (number of stop words, foreign words, “difficult” words, etc.).
• Structural features that reflect text organization (paragraph lengths, chapter lengths, etc.).
• Using these features formulas can be derived to identify the writing style of an author. 
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•  Stylometry-based methods can be used in internal and external plagiarism detection.