Features, techniques and evaluation in predicting articles' citations: A review from years 2010-2023

• Main Authors: Aiza, Wan Siti Nur, Shuib, Liyana, Idris, Norisma, Normadhi, Nur Baiti Afini
• Format: Article
• Published: Springer Verlag 2024
• Subjects: LB2300 Higher Education; Z Bibliography. Library Science. Information Resources
• Online Access: http://eprints.um.edu.my/44254/; https://doi.org/10.1007/s11192-023-04845-9

Robust findings of citations have a positive impact on researchers and significantly contribute to academic development. As a paper is cited more frequently or used as a reference in other articles, its citation count increases. Papers with higher citations tend to be more influential than those less cited. Research on predicting citation counts has evolved throughout the year in various fields. However, despite its recent growth, research on identifying commonly used features and techniques still lacks a comprehensive literature analysis. The present study addresses this gap and identifies frequently used features and existing techniques and their evaluation process for predicting an article's citations. This study reviewed 150 articles from 2010 to 2023, and selected 107 based on established exclusion and inclusion criteria. It provides an overview of publication features and the standard techniques used for their identification to facilitate improvements in this field. The findings indicate that previous works frequently used (i) selected features such as paper features and citation features in predicting citations and (ii) machine learning techniques that are commonly applied to predict article citations. These findings can provide beneficial information for researchers aiming to enhance their papers and maximize their impact.