Plagiarism
Plagiarism Detection - How To Detect It
Content stolen? Learn powerful ways to detect plagiarism and protect your work. From manual checks to AI, discover effective methods to uncover stolen work.
Plagiarism, or using someone else’s work without giving them credit can have serious repercussions not just in the world of academics, but also professionally and creatively as well. Because digital content can easily be copied and shared, the need to detect plagiarism has never been greater.
Fortunately, there are a variety of ways to uncover plagiarism, from traditional methods all the way to using software and even artificial intelligence. Let’s take a closer look at the many different ways that you can detect and uncover plagiarism.
Plagiarism has been around since the exchange of ideas, but our methods of sharing and reusing those ideas have become more and more refined. In the early days, experienced educators could closely read a student’s work and tell when a piece of work didn’t match their usual writing style or quality.
That teacher might head to a dusty old bookshelf to seek out a reference book or roll up a microfiche to further analyze how the work was cited. To ensure they got their references right, students would often carry around grammar books and style guides that outlined the precise format of how works needed to be cited.
In some cases, there might be no works cited page at all, or the citations would be sloppy. If a works cited page resembled another student’s work, you can bet that assignments and references were often shared and copied, which led to academic penalties or at worst, expulsion.
Peer review was and still is a quite common way of checking works, not just for plagiarism but also authenticity. One professional or teacher alone may not recognize an idea or statement as being from a specific source, but others may, which is why peer reviews are still quite common in a variety of disciplines.
With the advent of the digital age came search engines like Google and others. For professors and professionals alike, search engines were a boon for plagiarism detection, as they allowed them to simply copy and paste a snippet of text into the search engine and find its exact match elsewhere on the web.
However, sly students and professionals knew that in order to get away with plagiarism, they needed to stay one step ahead of what could be found on the web, and went about lifting sometimes vast amounts of text directly from the source: an academic journal or other document that may not be found in a typical search engine.
It wasn’t long before software and tools emerged that allowed professors, managers and other professionals to check for plagiarism by directly searching a whole host of journals, sources and databases that may not be accessible with a simple Google search.
To take plagiarism detection one step further, some programs like TurnItIn, integrated directly with the Learning Management Systems (LMS) of many schools, colleges and universities, making it even easier for professors to check the work of multiple students for plagiarism by comparing their papers and essays to those already in TurnItIn’s vast database.
Other types of programs like Copyscape, were made to find duplicate content on other web pages. And in still other cases, the students themselves didn’t want to risk their grades or potential scholarships by plagiarizing, and wanted to check their work themselves to make sure they were fair about attributing their sources.
For that, Grammarly, an online grammar and spell checker, developed a plagiarism checker as a paid resource. With it, students could not only get help citing their sources, but they could also make sure they didn’t borrow too heavily and inadvertently commit plagiarism without realizing it.
Alongside the rise of plagiarism detection software came the ability to check the differences between two texts. These tools compare text or code side by side and highlight the differences between them. Such tools can be used in a professional capacity as well as in academia to check for plagiarism as well as compare files.
Beyond having “another set of eyes” (or two or dozens) looking at a paper, or running it through a comparison checker, new technologies have made it easier to check for plagiarism. Understandably, so too have those wanting to commit academic or professional fraud found ways around things like plagiarism checkers by way of paraphrasing, drawing from multiple sources and combining them into one and using other tactics to circumvent computers and websites.
With the explosion of AI tools and resources, students and professionals have an even greater tool in their arsenal – a way to use machine learning and pattern detection to write in a sometimes-convincingly human way. Although this can save a great deal of work and time, it also presents an issue in that the AI pulls from different sources (or makes them up on-the-fly), creating greater opportunities for plagiarism or at the very least, AI writing that has neither the nuance nor the complexity of human writing.
These advanced plagiarism detection tools may not be getting their time in the limelight the way AI writing has, but rest assured that many of these features are present in modern AI writing and plagiarism detectors.
Unlike traditional plagiarism detection using third party databases or software, intrinsic plagiarism detection doesn’t rely on external databases. Instead, it analises a document to see if there are several different writing styles present – a key hallmark that may suggest plagiarism. Intrinsic plagiarism detection is based on the belief that every writer has their own unique style and tone, and that this shines through in their writing.
Whenever a document is created for the first time, the computer creates information about that document that can be used to retrieve it later. This information includes things like when it was created, the last time it was edited, who edited it and so on. Much of this information isn’t visible in the document itself, but can be found easily enough with some deeper investigation. In this way, it’s possible to find the origin of the document and thus who the idea or content belongs to.
In the world of academia, with so many different databases of peer-reviewed literature, studies and research, occasionally papers are retracted. This happens whenever, for example, a scientific article is found to have its data falsified or fabricated, or if a work is plagiarized. In other cases, the way information was conducted might cause a paper to be retracted, or there may be disputes between authors.
However the retraction occurred, there exist databases that make a note of it. If a document was highlighted for retraction for plagiarism, these databases can be searched in order to find similar documents that may have borrowed from it. The most well-known database of this type is called Retraction Watch. Not only does it track each retraction but also includes detailed reasoning behind why the paper was removed.
Building on the idea of intrinsic plagiarism, stylometry takes it a step further. Every writer has their own “linguistic style”. The way they use words, structure sentences, and even use punctuation is like their “writing fingerprint”. Stylometry takes this idea and turns it into a science, scanning for possible areas where the sentence structure or punctuation style doesn’t match the author’s regular writing style.
Just like with metadata analysis, document fingerprinting is another way of detecting plagiarism. With larger documents, instead of comparing the text word for word (which would take a lot of time and computing power) document fingerprinting allows the document to be broken down into “chunks” (called tokens). and then passed through an algorithm that creates a “hash” of the chunk.
The “hash” is like its digital fingerprint, and where two chunks have the same fingerprint, the program notes it as potential plagiarism. This is one of the methods that our own Originality.AI plagiarism detector and AI writing detector uses in order to detect plagiarism. Because only parts of a document are used, the process is incredibly efficient and can be scaled to handle numerous documents. What’s more, the fingerprinting and flagging of potential plagiarism can be tweaked to be incredibly sensitive or more flexible depending on the user’s needs.
New advances in AI writing and plagiarism detection are being developed and launched that don’t just look at the words on the page, but look at the inherent meaning behind them This allows for the flagging of one of the most common, but also one of the hardest to detect, types of plagiarism: paraphrasing.
What’s more, new technology is being developed that not only checks for plagiarism in text, but also analises audio waveforms to find duplicate beats or notes in music or speech, as well as plagiarism checkers that look at the angles of images or diagrams to see if a work has been copied or a derivative has been made. Other plagiarism detection tools work with multiple languages to see if a work has been plagiarized from a foreign language and translated into English.
As you can see, plagiarism detection is much more than finding the same text or passages in a given work. As technology has gotten more advanced, those looking to plagiarize have gotten more and more crafty at avoiding detection. Although it may seem like AI has widened the gap considerably, the same technology that makes it possible for AI to write in a human-like style is also making it possible to detect the tell-tale signs of AI writing.
Plus, AI detection tools like Originality.AI are always being updated, with a greater emphasis on accuracy and precision and fewer false positives. And although no plagiarism detection tool can detect 100% of plagiarism 100% of the time, we’re getting closer to narrowing the gap and making academia and the web a fairer place for all to write and publish.