Version 1
: Received: 28 October 2024 / Approved: 28 October 2024 / Online: 29 October 2024 (01:49:58 CET)
How to cite:
Chigarev, B. A Proof-of-Concept Methodology for Identifying Topical Scientific Issues in New Publications Whose Citations Have Not Yet Been Established. Preprints2024, 2024102220. https://doi.org/10.20944/preprints202410.2220.v1
Chigarev, B. A Proof-of-Concept Methodology for Identifying Topical Scientific Issues in New Publications Whose Citations Have Not Yet Been Established. Preprints 2024, 2024102220. https://doi.org/10.20944/preprints202410.2220.v1
Chigarev, B. A Proof-of-Concept Methodology for Identifying Topical Scientific Issues in New Publications Whose Citations Have Not Yet Been Established. Preprints2024, 2024102220. https://doi.org/10.20944/preprints202410.2220.v1
APA Style
Chigarev, B. (2024). A Proof-of-Concept Methodology for Identifying Topical Scientific Issues in New Publications Whose Citations Have Not Yet Been Established. Preprints. https://doi.org/10.20944/preprints202410.2220.v1
Chicago/Turabian Style
Chigarev, B. 2024 "A Proof-of-Concept Methodology for Identifying Topical Scientific Issues in New Publications Whose Citations Have Not Yet Been Established" Preprints. https://doi.org/10.20944/preprints202410.2220.v1
Abstract
Identification of topical research issues using bibliometric data is complicated by the fact that the citation of publications from recent years has not yet been formed. In this paper, it is proposed to use the average citation of the journal over two years rather than the article citation to estimate to estimate the weight of the keyword occurring in the sample under consideration. In order to identify the terms that characterize relevant research topics, it is proposed to represent the term co-occurrence network in coordinates of the average occurrence of the term per year and the average normalized citation of the term to visualize the graph. Furthermore, this methodology proposes the use of preprocessing of keywords using a lemmatization dictionary. 3,696 bibliometric records for 2022–2024 from the ScienceDirect platform on the topic of industry digitalization were used for the analysis. The VOSviewer and Scimago Graphica programs were used sequentially. The former was used to display the overall landscape of the study, while the latter was used to analyze in more detail the individual slices of bibliometric data obtained with VOSviewer. A ‘convex hull’ was used to facilitate the perception of cluster boundaries. After analysing the data and highlighting the terms, it is proposed to provide context by quoting strings from publications and defining of lesser-known terms. The industry digitalization is not only a technical and technological issue but also an economic one, as evidenced by terms such as ‘digital economy’ and ‘Industry 5.0’.
Keywords
topical research issues; keyword weight estimation; lemmatization; VOSviewer; Scimago Graphica; industry digitalization
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
