Plagiarism detection is increasingly becoming a major challenge in the digital era due to the extensive availability of content over the web and the growing utilization of innovative and improved methods by plagiarists to modify content to a high degree that can evade detection even by the most effective content scanning software programs. Thus, artificial intelligence (AI) powered plagiarism detection solutions are being used to detect plagiarism more accurately and effectively. This article discusses the importance and application of AI for plagiarism detection.
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Importance of AI in Plagiarism Detection
The application of AI technologies has led to significant advancements in plagiarism detection. AI-powered plagiarism detection tools primarily leverage sophisticated machine learning (ML) techniques and algorithms to analyze textual content, detect similarities, and identify potential instances of plagiarism.
These AI-powered tools can process substantial volumes of data to compare documents against massive databases of online content, publications, and academic sources. Plagiarism detection systems can provide more reliable and comprehensive results by reducing false negatives and positives using AI.
Advanced text matching algorithms are one of the critical contributions of AI in plagiarism detection. These AI-powered algorithms employ different approaches, such as semantic analysis, fingerprinting, and string matching, to identify potential instances of plagiarism.
AI enables these algorithms to perform at a speed and scale exceeding manual detection methods, which significantly improves the plagiarism detection process. Several AI techniques, such as natural language processing (NLP), are critical in detecting rephrased and paraphrased content.
NLP algorithms can analyze linguistic patterns, semantic structures, and syntax to identify instances where plagiarists have altered the structure and wording of the original text to conceal plagiarism, which substantially enhances the overall effectiveness of plagiarism detection.
Additionally, AI-powered plagiarism detection solutions can adapt and learn from data. For instance, ML algorithms can be trained using large datasets containing known plagiarism cases to enable these algorithms to identify indicators and patterns of plagiarism with higher accuracy.
The performance of these algorithms improves over time as they can learn from experience, leading to continuous improvement in the effectiveness of the plagiarism detection process. AI also increases the efficiency of plagiarism detection systems by rapidly processing and analyzing documents, which enables scanning in real-time and instant feedback.
In academia, this feature is extremely beneficial for both educators and students as it allows quick identification of potential plagiarism and timely intervention to address such issues and conserves valuable time for educators, enabling them to focus on providing guidance and quality feedback to students.
Plagiarism detection is primarily classified into extrinsic plagiarism detection and intrinsic plagiarism detection. Extrinsic plagiarism detection involves the comparison of suspicious documents to a collection of genuine documents/reference collection to identify plagiarized content, while intrinsic plagiarism detection involves analysis of the input document to detect plagiarism without using any reference collection for comparison. Intrinsic detection methods use stylometry to examine the linguistic features of a text to detect changes in the writing style within the document, which are considered potential plagiarism indicators.
Techniques/Algorithms in AI-powered Plagiarism Detection
ML Techniques: Classification algorithms, such as support vector machines (SVM) and random forests (RF), trained using labeled datasets, can detect patterns and classify text segments as original or plagiarized.
Similarly, unsupervised learning algorithms, such as hierarchical clustering and k-means clustering, can group similar text segments together to assist in detecting clusters that can indicate plagiarism.
Additionally, deep learning models, such as recurrent neural networks and convolutional neural networks (CNN), can learn complex patterns in text data, which can improve plagiarism detection accuracy.
Text Matching Algorithms: The Jaro-Winkler distance and the Levenshtein distance algorithms can be used to measure the similarity between strings to detect near identical or identical text segments.
Similarly, the Rabin-Karp and the Winnowing algorithms can generate hash-based fingerprints of text segments to facilitate efficient comparison and matching, while techniques such as latent Dirichlet allocation (LDA) and latent semantic analysis (LSA) can investigate the semantic context of documents to detect related content even when the wording is paraphrased or altered.
NLP Techniques: NLP techniques, such as part-of-speech (POS) tagging, can identify the category and role of words in a sentence to assist in detecting the structural similarities between several documents.
Similarly, named entity recognition (NER) algorithms can classify and identify named entities, such as locations, organizations, and people, in text, to detect instances of paraphrasing or copying of such entities.
Semantic role labeling can reveal dependencies and relationships between text segments by identifying the semantic roles of words in sentences, improving plagiarism detection capabilities.
Cross-language Plagiarism Detection Techniques: Machine translation systems powered by AI can translate documents in different languages into a common language for comparison to facilitate cross-language plagiarism detection.
Additionally, several algorithms can extract language-independent features, such as stylometric features or character n-grams, to allow comparison and detection of plagiarism across various languages.
Gradient boosting regression trees, SVM, recurrent artificial neural networks (RANNs), k-nearest neighbor, homotopy-based classification, Naïve Bayes (NB), equal error rate, decision tree (DT), RF, genetic algorithm (GA), and multilayer perceptron (MLP) are the ML-based intrinsic plagiarism detection methods that can be used for author verification, author clustering, author identification, and style-breach detection.
SVM, NB, DT, k-nearest neighbor, linear discriminant analysis, GA, logical regression model, RF, ANN, gradient boosting, abductive networks, linear regression, L2-regularized logistic regression, Gaussian process regression, ridge regression, isotonic regression, and deep neural networks (DNN) are the ML-based extrinsic plagiarism detection methods that can be employed for document level detection, candidate retrieval, detailed analysis, and paraphrase identification.
Limitations of AI in Plagiarism Detection
Limited Detection from Non-text Sources: AI-powered plagiarism detection tools cannot effectively detect plagiarism from non-text sources, such as audio files, videos, and images, as they have been primarily designed to analyze text data.
High Dependence on Data Quality: The effectiveness and accuracy of AI-powered detection tools are highly dependent on the quality of data analyzed by them. Thus, the accuracy of these plagiarism detection tools can be affected if the data is outdated, incomplete, or inaccurate.
Insufficient Understanding of Context: AI-powered plagiarism detection tools cannot effectively understand the context based on which a particular text has been produced, which can impact their ability to detect potential instances of plagiarism. For instance, a detection tool can classify a text as plagiarized even when the text was cited properly, as it lacks the understanding of the context in which citation was used.
Recent Advancements
In a paper published in the Journal of Intelligent Systems, researchers developed a monolingual plagiarism detection technique to tackle paraphrased plagiarism cases. This paraphrase recognition approach can be utilized to identify instances of plagiarism in source and suspicious passages.
An SVM-based paraphrase recognition system, which functions by extracting semantic, syntactic, and lexical features from the input text, was used in the study. Researchers investigated the system on three corpora, including Wikipedia Rewrite Corpus, a subset of METER, and Webis CPC, at both sentence and passage levels.
The proposed system demonstrated a better performance at the passage level compared to the sentence level and a comparable or better performance compared to the best-performing system on the three corpora. Moreover, the system also displayed good performance when it was tested on different subcategories of the P4P corpus. Thus, the findings showed that paraphrase recognition techniques can be effectively employed for the development of plagiarism detection systems.
To summarize, integrating AI in plagiarism detection systems is increasing the efficiency and accuracy of plagiarism detection. Moreover, further improvements in detection are expected with the continuous evolution of AI technologies. Deep learning techniques, including recurrent neural networks and CNNs, hold significant potential in improving plagiarism detection accuracy. Thus, more research is required to develop sophisticated neural network architectures that can easily and effectively capture semantic relationships and complex patterns in text.
References and Further Reading
The role of AI in content plagiarism detection [Online] (Accessed on 01 October 2023)
Mishra, S. (2023). Enhancing Plagiarism Detection: The Role of Artificial Intelligence in Upholding Academic Integrity. Library Philosophy and Practice, 7809. https://digitalcommons.unl.edu/libphilprac/7809
Chitra, A. & Rajkumar, A. (2015). Plagiarism Detection Using Machine Learning-Based Paraphrase Recognizer. Journal of Intelligent Systems. https://doi.org/10.1515/jisys-2014-0146.
Foltýnek, T., Meuschke, N., Gipp, B. (2019). Academic Plagiarism Detection: A Systematic Literature Review. ACM Computing Surveys, 52, 1-42. https://doi.org/10.1145/3345317.