Fighting Plagiarism with Machine Learning and Big Data Analytics

Fighting Plagiarism with Machine Learning

The World Wide Web is a plagiarist’s dream. Huge amounts of digital content are readily available, and they can be easily copied. Fortunately, there’s hope for people fighting plagiarized content. Using data analytics and machine learning, plagiarism can be kept in check.

Fighting Plagiarism with Machine Learning and Data Analytics

Plagiarism, of course, is the act of reproducing someone else’s work or ideas, in whole or in part, without giving credit to the original creator.

Plagiarism can involve verbatim copying of text, images or other media, but it is not limited to that. Content that borrows someone else’s ideas without proper attribution is also a form of plagiarism. So are articles or blog posts that are republished without permission of the original content owner, or which are reproduced without the byline of the original author.

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Teachers, college professors, and content publishers are fighting a constant battle against plagiarism – which, again, has become an especially easy pursuit in the age of the internet, which places so much information at the fingertips of anyone seeking to misappropriate it.

Using Data Analytics to Combat Plagiarism

Yet even as digital technology has made it easier to plagiarize, it is also enabling new anti-plagiarism tools, which are powered by data analytics and machine learning.

There is now a wide range of tools available to detect plagiarized content. (Here is a list of just some.)

While these tools differ in the details of their functionality, they all share the same basic approach to detecting and analyzing plagiarized content. They leverage data and machine learning to make automated decisions about whether one piece of content is similar enough to another to be considered plagiarism.

Fighting Plagiarism with Machine Learning and Data Analytics

The Complexities of Plagiarism Detection

Detecting plagiarized material is easy enough when verbatim copying of content has occurred.

As noted above, however, not all plagiarism takes this form.

Catching plagiarists gets more complex when you are dealing with what’s called “smart plagiarism,” which means plagiarized content that is deliberately designed to avoid detection tools.

Smart plagiarists might reuse someone else’s content, but change around words or sentence structures to throw off the checkers. This can be done manually (as in the case of students trying to avoid writing an original term paper, for example) or automatically (such as by digital tools that steal content from one website and republish it in modified form on a different site).

Smart plagiarism can be fought only with smart data analytics and machine learning. Sophisticated tools need to be driven by complex algorithms that can analyze the similarities between two pieces of content and determine whether one was copied from the other, even if the items appear original from a superficial perspective.

Conclusion

Combating plagiarism through machine learning is yet another example of how data is now driving virtually everything we do. Data helps keep students, websites and everyone else on the internet honest, even in cases where it would otherwise be very hard to do so.

Making the most out of data for plagiarism detection and any other purpose requires tools for transforming, moving and analyzing data efficiently. Syncsort provides those Big Data solutions.

You can also check out Syncsort’s eBook “Mainframe Meets Machine Learning,” to learn how machine learning could help alleviate the most difficult challenges and issues facing mainframes today.

Christopher Tozzi

Authored by Christopher Tozzi

Christopher Tozzi has written about emerging technologies for a decade. His latest book, For Fun and Profit: A History of the Free and Open Source Software Revolution, is forthcoming with MIT Press in July 2017.

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