This project aims to develop a system that automatically detects plagiarism in online content. This system will be designed to work with a web-based content management system (CMS), such as WordPress, Joomla, or Drupal. The system will be able to scan articles or blog posts published through the CMS and compare them against a database of known plagiarized content. If any matches are found, the system will flag the article for review by the site administrator (Suryanovika, n.d.).

Introduction

This project aims to develop a system that automatically detects plagiarism in online content. This system will be designed to work with a web-based content management system (CMS), such as WordPress, Joomla, or Drupal. The system will be able to scan articles or blog posts published through the CMS and compare them against a database of known plagiarized content. If any matches are found, the system will flag the article for review by the site administrator (Suryanovika, n.d.).

There are many existing solutions for detecting plagiarism, but most require manual user input. For example, Google Scholar allows users to search for similar articles but does not provide any automated way to check for plagiarism. Similarly, Copyscape is a popular service that can be used to check for plagiarized content, but it requires users to enter each URL manually. There are also several plugins available for WordPress that claim to be able to detect plagiarism, but they all require manual input from the user.

This project aims to develop an automated solution that can be integrated with a web-based CMS and does not require manual input from the user. This would make it much easier for website administrators to check for plagiarized content and would help reduce the amount of copyright infringement on the internet.

Literature Review

There is a large body of literature on copyright infringement and plagiarism detection. In general, copyright infringement refers to the unauthorized use of copyrighted material, while plagiarism is defined as “the unauthorized use of someone else’s original work without giving credit” (Kale, 2020). Copyright infringement can occur when someone copies or distributes copyrighted material without permission from the copyright holder; however, it should be noted that copyright law does allow for specific uses of copyrighted material without permission under what is known as the “fair use” doctrine. Plagiarism detection systems typically work by comparing new documents against a database of known sources; if any matches are found, the document is flagged as potentially plagiarized (Tripathi et al., 2021).

Many different approaches have been proposed for detecting plagiarism; however, most existing solutions require user manual input and are not well-suited for integration with web-based content management systems (CMSs). For example, Google Scholar provides a way for users to search for similar articles; however, there is no automated way to check whether or not these articles have been copied without attribution (google scholar Help Center.

Methods

The proposed system will use natural language processing (NLP) techniques to compare articles against a database of known plagiarized content. A machine learning algorithm will be used to learn new plagiarism patterns and improve detection accuracy over time. The system will be designed to run periodically in the background to scan new articles as they are published on the site. Administrators will be notified if any matches are found so that they can take appropriate action (Chavan et al., 2021).

This project aims to develop an automated plagiarism detection system that can be integrated with popular web-based CMS platforms. The system should be able to detect plagiarism accurately and notify administrators so they can take appropriate action. In order to achieve this goal, the following objectives must be met:

1. Develop a working prototype of the plagiarism detection system, which can be integrated with a web-based CMS platform such as WordPress, Joomla, or Drupal;
2. Evaluate the accuracy of the prototype using a dataset of known plagiarized and non-plagiarized content;
3. Improve accuracy by implementing additional features such as support for multiple languages and better handling of synonyms;
4. Write documentation for installation and usage instructions for administrators who want to integrate the tool into their site;
5. Deploy prototype on live website for further testing under real-world conditions.

Resources

The development of the proposed system would require several resources. First, a database of content would need to be compiled. This database would need to be constantly updated in order to be effective. Second, a software program would need to be created to scan online content and compare it to the database. This software would need to be constantly updated as well. Third, a team of experts would need to be assembled to review the results of the scans and determine if plagiarism had occurred. This team would need to be able to update the software as needed. Finally, a system would need to be implemented to notify the original content creators when their work has been plagiarized. This system would need to be constantly monitored and updated.

Project Plan

The project to develop a system to detect plagiarism on online content automatically will require the following tasks to be completed:

1. Research plagiarism detection methods and select the most appropriate one for the project.
2. Develop the plagiarism detection system according to the chosen method.
3. Test the plagiarism detection system on a variety of online content.
4. Evaluate the results of the tests and make improvements to the system if necessary.
5. Launch the plagiarism detection system.

The timeline for this project is as follows:

1. Research: 1 week
2. Development: 2 weeks
3. Testing: 1 week
4. Evaluation and improvement: 1 week
5. Launch: 1 week

References

Chavan, H., Taufik, M., Kadave, R., & Chandra, N. (2021). Plagiarism Detector Using Machine Learning. International Journal of Research in Engineering, Science and Management, 4(4), 152-154.

Kale, S. T. (2020). Use of Turnitin and Urkund anti-plagiarism software tools for plagiarism detection/similarity checks to the Doctoral theses: Indian experience. Cadernos BAD, pp. 1, 95-99.

Suryanovika, C. ONLINE PLAGIARISM DETECTOR SOFTWARE TO ASSESS STUDENTS’ACADEMIC INTEGRITY. ELT in Asia in the Digital Era: Global Citizenship and Identity, 486.

Tripathi, A., Gupta, S., & Yadav, S. S. (2021, March). Plagiarism Detector using Long Term Common Tracking. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp.