Predicting citation impact of academic papers across research areas using multiple models and early citations 2024

As the volume of scientific literature expands rapidly, accurately gauging and predicting the citation impact of academic papers has become increasingly imperative. Citation counts serve as a widely adopted metric for this purpose. While numerous researchers have explored techniques for projecting papers’ citation counts, a prevalent constraint lies in the utilization of a singular model across all papers within a dataset. This universal approach, suitable for small, homogeneous collections, proves less effective for large, heterogeneous collections spanning various research domains, thereby curtailing the practical utility of these methodologies. In this study, we propose a pioneering methodology that deploys multiple models tailored to distinct research domains and integrates early citation data. Our approach encompasses instance-based learning techniques to categorize papers into different research domains and distinct prediction models trained on early citation counts for papers within each domain. We assessed our methodology using two extensive datasets sourced from DBLP and arXiv. Our experimental findings affirm that the proposed classification methodology is both precise and efficient in classifying papers into research domains. Furthermore, the proposed prediction methodology, harnessing multiple domain-specific models and early citations, surpasses four state-of-the-art baseline methods in most instances, substantially enhancing the accuracy of citation impact predictions for diverse collections of academic papers.

Autores	Vilela, JMD; Faria, JR
Título	Industry 4.0 and 5g technology on firms network: a balanced competitive expansion conceptual model development
Afiliaciones	Universidade Federal Fluminense
Año	2022
DOI	10.14488/BJOPM.2022.1426
Tipo de acceso abierto	gold
Referencia	WOS:000811447300003
Artículo obtenido de:	WOS

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Predicting citation impact of academic papers across research areas using multiple models and early citations

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