I was surprised to see the results after writing “How does a plagiarism checker work” in the Google search bar.
Guess the first answer popped up?
Can Plagiarism Checker be Cheated!
No! I didn’t ask that. I wanted to ask the algorithm behind a general plagiarism checker software. It was supposed to be answering something like this:
Plagiarism checker software use sophisticated natural language processing algorithms along with crawling, indexing, and gathering information through a huge corpus of thousands of web-pages, PDFs, and several documents scattered over the internet in order to work.
This could be the shortest definition of the above question. Even though you want to trick plagiarism checker by using simple steps, you need to dig a little deeper to stay updated.
I can remember how changing colours and adding bogus characters in your papers could be used to bypass tools a few years ago. The time has long gone now. These tricks work only for some free plagiarism checkers available online now.
Another trick which was quite popular among students, using images instead of text because image processing and gathering information from images are still under process. But let me tell you that the time is near. As Google lenses have come to the pictures now with quite advanced image processing mechanisms, many innovative companies have started working in the same direction. Soon, the trick will go outdated.
Anyway, let’s put our attention to our actual concern: How do a general plagiarism detection software work?
In the following words, I will try to avoid technical discussion as the article is written by considering a general user in mind.
What is Plagiarism?
Plagiarism is any kind of theft. Whether it is media, text-based content, or a concept map. Most of the discussion about plagiarism and detection revolves around text-based content and in academia. However, there are many popular cased around the globes where plagiarism has been seen in Hollywood movies, poetry, literature or in other industries.
Plagiarism Detection – Working in Text-Based Articles
We will narrow down our discussion to the plagiarism in articles, blog posts and other similar content.
Matching Text One by One
String matching is one of the most popular ways to detect plagiarism today. A longer string (string is the sequence of characters) is shortened into smaller substrings and compared against the suspicious resource. This approach has been adopted heavily to detect plagiarism from external sources. Since it requires a significant amount of data to be stored in order to compare, it is the non-viable approach to compare against larger databases.
Looking for Similar Fingerprints
Fingerprinting is perhaps the most widely used approach in plagiarism checking. In this approach, a representative digest of documents is formed by analysing and creating sub-strings. It is later used to check for similarity against a suspicious source. If the threshold of a pre-defined percentage of similarity is passed, the document shows the potential plagiarism.
Since it works by making quicker digests and doesn’t compare in whole; this method gives faster results and accurate.
Other Ideas to Detect Plagiarism
Thanks to the enhancement in technology and huge research in the field that now we have image processing algorithms which use vector analysis of text and build the corpus of strings. This can later be checked against the suspicious source.
Comparision Based Plagiarism Detection
Python (a programming language) is one of the most widely used algorithms to build custom comparison programs. One such is py code-similar which can compare similarity by detecting similar strings in both the documents. The similar approach is used in MOSS (Measure of software similarity) to detect plagiarism in code.
Factors governing the working of plagiarism detection software
Wikipedia has already summaries well on this topic. Here are some of the most important ones to get an idea.
- Scope of detection: Resources, File types, government resources, public libraries, private directories, institutional databases etc.
- Analysis Time: How much time a plagiarism checker takes to show the results.
- Document Capacity: How many documents a software can process at a time. this is quite important in academic submissions.
- Result Accuracy: Precision and recall is a measured factor. High precision shows less false positives in the result. The recall is calculated by comparing total flagged sources as plagiarized to the actual number of documents which were plagiarized.
What Lies in Future – Bottom Lines
Plagiarism detection is going to see the big improvement in the near future. Hiding in PDF character maps, finding ideas in images will probably won’t’ work. Free tools (as always) won’t be enough to find similarity. Machine learning algorithms have given exponential growth to the development of context-based understanding.
An important thing to keep in mind here is, the similarity is not plagiarism.
For example: “The ocean is blue” is similar to “Most of the sea colour is blue” but it might not be plagiarized (or it might be). It is a challenge to keep someone in the right category. Let’s see.