Automated Detection of Plagiarism in Academic Papers

Introduction

Plagiarism detection in academic papers is crucial for maintaining the integrity of scholarly work. With the increasing availability of digital content, there is a growing need for automated systems that can efficiently and accurately detect instances of plagiarism. This project proposal presents a framework for developing such a system, inspired by current research in the field.

Background

Recent advancements in computational methods have significantly improved the ability to detect plagiarism. These methods include analyzing textual similarity at lexical, syntactic, and semantic levels. The use of machine learning techniques, such as natural language processing (NLP) and deep learning, has been particularly effective in identifying subtle forms of plagiarism, such as paraphrasing and summarization.

Project Objective

The primary objective of this project is to create an automated plagiarism detection system that can accurately identify plagiarized content in academic papers. The system will employ a combination of machine learning algorithms and linguistic analysis techniques to enhance detection accuracy.

Methodology

1. Data Collection and Preprocessing

• Dataset: Utilize the PAN Plagiarism Corpus (PAN-PC), which includes both manually and automatically inserted plagiarized texts[3].
• Preprocessing: Clean and preprocess the data to ensure consistent formatting and remove any noise that could affect the analysis.

2. Feature Extraction

• Linguistic Features: Extract features such as n-grams, tf-idf values, and semantic similarities using NLP tools.
• Structural Features: Analyze document structure, including citation patterns and section headings.

3. Model Development

• Machine Learning Models: Implement models such as Support Vector Machines (SVM) and neural networks to classify documents as plagiarized or not.
• Evaluation Metrics: Use precision, recall, and F1-score to evaluate model performance.

Expected Outcomes

The proposed system is expected to achieve high accuracy in detecting various forms of plagiarism, including verbatim copying and paraphrasing. By leveraging advanced computational methods, the system should provide reliable results that can be used by academic institutions to uphold scholarly standards.

Conclusion

This project aims to contribute to the field of academic integrity by developing a robust automated plagiarism detection system. By incorporating state-of-the-art machine learning techniques and comprehensive datasets, this system will enhance the ability to detect and prevent plagiarism in academic writing.

For further details on related research, please refer to the paper "Automated Detection of Plagiarism in Academic Papers," available at https://ieeexplore.ieee.org/document/8768831. The dataset used for this project is available through the PAN Plagiarism Corpus (PAN-PC).