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A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field
Yousef A Alohali1, Mahmoud S Fayed1, Tamer Mesallam2
1College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
Biomed Research International
|August 1, 2022
Summary
Predicting scientific paper citations is challenging. This study found that machine learning models, particularly neural networks, can improve citation prediction by analyzing otology paper abstracts, with abstracts showing the most influence.
Area of Science:
- Otolaryngology
- Bibliometrics
- Computational Linguistics
Background:
- Scientific impact is often measured by citation counts, which are difficult to predict due to their skewed distribution.
- Improving citation prediction can enhance research visibility and impact assessment.
Purpose of the Study:
- To identify factors influencing scientific paper citation numbers in the field of otology.
- To develop and evaluate a machine learning approach for predicting paper citations.
Main Methods:
- Utilized machine learning (ML) and natural language processing (NLP) to analyze English text from scientific papers.
- Implemented and compared various algorithms including linear regression, boosted decision trees, decision forests, and neural networks.
- Developed the solution using visual programming on Microsoft Azure ML and Programming Without Coding Technology.
Main Results:
- Neural network regression analysis indicated that paper abstracts significantly influence citation numbers in otology research.
- The proposed ML/NLP solution demonstrated potential for improving citation prediction accuracy.
Conclusions:
- Machine learning models offer a viable method for enhancing the prediction of scientific paper citations.
- Optimizing research paper abstracts using ML insights may lead to increased citations and broader scientific impact.
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