Evaluation of Machine Learning to Early Detection of Highly Cited Papers

EasyChair Preprint 7502

Abstract

As one of the fastest-growing topics, machine learning has many applications that span through different domains including image and signal recognition, text mining, information retrieval, robotics, etc. It enables information extraction and analysis for better insights and decision-based systems. The Web of Science(WoS) citation database is a leading organization that provides citation data of high-quality published research. WoS has its metrics to label published articles as Highly Cited Paper(HCP). Machine learning (ML) can help researchers in identifying the key characteristics of HCP. Moreover, it can allow research evaluation units to forecast significant scientific articles. In other words, it may allow researchers and/or research evaluators to detect potential scientific breakthrough ideas and stay current. In this study, more than 26 thousand records of published articles indexed by WoS were analyzed. All the records are drawn from the Technology research area as defined by WoS. Four ML algorithms are evaluated to verify the HCP common factors influence in raising citations and interest in scientific articles. The ensemble algorithms show promising results to identify HCP articles using only four factors.

Keyphrases: Decision Tree, Highly Cited Paper, Highly-cited Research, Machine Learning Algorithm, Random Forest, bibliometric analysis, citation count, digital libraries, long term citation impact, longterm citation, machine learning, machine learning method, neural network, random forest adaboost svm, research field, scientific article

@booklet{EasyChair:7502,
  author    = {Galal Binmakhashen and Hamdi Al-Jamimi},
  title     = {Evaluation of Machine Learning to Early Detection of Highly Cited Papers},
  howpublished = {EasyChair Preprint 7502},
  year      = {EasyChair, 2022}}