How Do Plagiarism Checkers Work?
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Understanding how plagiarism detectors work will help you write more effective SEO posts. The originality of texts is one of the factors taken into account in SEO ranking. This allows search engines to filter out duplicate information that detracts from the user experience.
Sometimes content plagiarism is unintentional. As a content creator, you must remain very vigilant to avoid any penalization. Likewise, if you are outsourcing the writing of your texts, it is imperative that you ensure their relevance and authenticity.
However, given the volume of data circulating online, it is impossible to verify authenticity without the help of plagiarism software. Verification tools use different methodologies, from simple comparisons to sophisticated machine learning techniques.
In looking at how do plagiarism detectors work, it is necessary to refer to indexing and crawling processes. Although these are functions of web search engines, they are also indispensable for verifying originality.
Plagiarism tools identify similar content on the Internet, using search technology and data matching algorithms. From this evolves the need to build a corpus of referential texts for evaluating and comparing texts. In other words, the corpus is a database that compiles information from online sources, books and other documents. The larger the database, the more the software can analyze. This means greater quality and reliability of the plagiarism software.
As this is a daunting task, it is not surprising that some verification programs share databases. In fact, many use the Turnitin database, as it is the most robust and comprehensive. It began as a peer review resource, focused on the academic environment. Its purpose was not to detect plagiarism, but to look for similarities with other publications. It has been so effective that it is now a paragon for other plagiarism tools such as Scribbr.
Identification of text matches
In understanding how do plagiarism detectors work, it is important to consider the matching procedure. It is impossible to determine the intention of a user publishing duplicate content. Plagiarism software can only calculate how similar content is to other publications.
The process involves complex mathematical operations and the application of coding and machine learning principles. The most widely used method is cosine similarity checking.
First, convert the text into vectors, i.e. represent the content sets in an algebraic model. For example, one-hot encoding allows data to be labeled and assigned values. This generates an identification vector that can be compared with other vectorized content.
If we analyze a collection of words, the reference term will receive the value 1 and the others will be equal to 0. Thus, in the coordinate plane, all points of the vector will be located at 0, except the point corresponding to the selected word.
Take, for example, a text with vocabulary to be examined composed of the terms: landscape, blue, natural, night. If the keyword is “natural,” one-hot encoding will draw a vector with positions 0,0,1,0.
Once the terms have been converted into vectors, they can be compared mathematically. Plagiarism tools calculate the magnitude or size of the vector space to establish the similarity of the contents. They determine the cosine of the angle formed by the two vectors and apply the scalar product formula.
The result is expressed in a range from 0 to 1. A value of 1 indicates a complete content match. Values closer to 0 represent a lower degree of similarity of the texts.
Other plagiarism detection methods
Cosine similarity checking is the basis of many plagiarism detection systems. For example, Word2vec combines the application of cosine similarity with natural language processing techniques. This technique identifies terms with semantic similarities. Therefore, it is able to detect rephrased content.
It is also important to pay attention to Google updates, as they impact how plagiarism detectors work. In this regard, the innovation brought about by the launch of BERT is noteworthy.
BERT stands for Bidirectional Encoding of Transformer Representations. The key here is bidirectionality. This means that both the terms preceding the keyword and the terms following the keyword are analyzed. The aim here is a better understanding of the context and intent of the search.
The development of artificial intelligence has been key in creating this model. This principle can also be applied to plagiarism detection, as simple paraphrasing is not enough to create original content. This is essential for evaluating the quality of your publications and their SEO ranking.
Of particular note is the application of Siamese LSTM which is Siamese architecture of long-term attention to identify semantic similarities. This method detects plagiarism with a high level of accuracy. Like cosine similarity checking, these techniques rely on neural networks to detect similar texts.
This is a rapidly expanding field, with new studies being published every day that delve deeper into the subject. Overall, simple keyword tracking is insufficient to detect plagiarism. Models are increasingly being enhanced by adding features such as style analysis and fingerprinting.
In addition to the obvious benefits that these bring to content creation, they are also important in other areas. They are applied to determine the validity and authority of scientific and academic papers and for police-work-related tasks.
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