The algorithms used to detect plagiarism

To identify whether someone's handed in piece has been stolen or not, there are various methods used. Human detection works just fine, but it is slow and easy to make mistake or errors, making algorithms more efficient. Various algorithms were therefore put up to help people detect or know before submitting, whether their work is considered original. These include string tiling, the Karp-Kabin, string matching, the SCAM, sequence matcher, the Hackel's, and Sherlock algorithms to name a few. Although the results are alike, each of the mentioned ways follows a unique approach, from stressing on a word to the whole paragraph.

The Hackel's method helps you find


The Hackel's method helps you find all the similar points in a text or piece until there are only the differences left. It enables you to observe how frequent a line appears in the given file(s) and rule out those that appear only once as non-plagiarized. For one that appears more than once, regardless of whether the words are interchanged, it's already subject to suspicion and further investigation. Next, adjacent lines from two or more files are checked to see if they are identical, allowing you to find blocks of moved lines. After a thorough check and comparison between the pieces in question, whether they will be deemed plagiarized depends on how different they are, the less different, the more they were copied.

Sherlock's algorithm compares similar lines from


Sherlock's algorithm compares similar lines from various documents and indicates that lines are not plagiarized if they have different sets of keywords. However, if the piece has numerous similarities, it is analyzed for plagiarizing. The number of alike words is divided by the total word number in the article and multiplied by 100% to come up with the plagiarized percentage. In case the outcome is on the higher side of the percentage scale, such work will be said to be plagiarized, especially when your result after the calculations is or exceeds 80%. Although this method may seem complicated and needs mastery of a little math, it's very efficient in reaching your desired goal as the figures and results at the end of the day are realistic.

An article about the algorithms used to detect plagiarism


String-matching algorithm enables the user to find repeated patterns or strings within a larger text. It is created in a way that detects any alikeness in the patterns or strings of a piece, after which the user is notified. This alternative might be slower though especially when there's a variable width encoding. To increase its efficiency, users can search for the sequence of code units, but only after specifically designing the encoding that fits it, to avoid getting false results. The real-time feedback during its use helps you analyze your work, make the necessary corrections and avoid any penalties or fines you could have incurred otherwise. It is highly efficient and accurate, but can only be used by webs that are capable enough to implement all that's needed so that one feature doesn't end up affecting the other.

Python's sequence matcher, difflib, helps find an output that is more acceptable for the user. It compares parts of an article and returns the ones that have the longest and shortest matching blocks. They mostly focus on the longer string which they call a haystack, and the shorter, referred to as the needle, helping find more occurrences of the latter within the former. The haystack could be a paragraph, and the needle a sentence, or they could be the work suspected to be copied and the one suspected to have been copied from. If more than two occurrences of the shorter string are found within the longer, the sequence maker is practically telling a user that someone plagiarized another's work. This method allows you to narrow down your search from a whole article to what you specifically need.

There are many ways used by software to help users avoid the results of a stolen piece by letting them know what an unoriginal text looks like. Rather than limit yourself to the cheaper human detection way, it is easier to part with an extra cent and get a service that is faster, more efficient and safer. Be it frequent item set analysis, string tiling, the Hackel's, the Sherlock's algorithms or grammarly's checker among others, you should apply whichever you can afford as some require more resources than others. Whichever one is applied, it is almost a need for plagiarism to end since it has become a big stigma to the research of today.

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