Abstract
In this study, employing the IEEE Xplore database as the data source, articles on different topics (keywords) and their usage data generated from January 2011 to December 2020 were collected and analyzed. The study examined the temporal relationships between these usage data and publication counts at the topic level via Granger causality analysis. The study found that almost 80% of the topics exhibit significant usage-publication interactions from a time-series perspective, with varying time lag lengths depending on the direction of the Granger causality results. Topics that present bidirectional Granger causality show longer time lag lengths than those exhibiting unidirectional causality. Additionally, the study found that the direction of the unidirectional Granger causality was influenced by the significance of a topic. Topics with a greater preference for article usage as the Granger cause of publication counts were deemed more important. The findings’ reliability was confirmed by varying the maximum lag period. This study provides strong support for using usage data to identify hot topics of research.
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Notes
Degree centrality refers to the number of direct connections a node has with other nodes (in a topic co-occurrence network, the nodes represent topics). Betweenness centrality measures the frequency of a node’s appearance on all shortest paths within the network, quantifying its role and importance as a mediator or “bridge” within the network. Closeness centrality is defined by calculating the reciprocal of the shortest path lengths from a certain node to all other nodes in the network, which is used to measure the average proximity of a node to all other nodes within the network. Eigenvector centrality involves the adjacency matrix of the network and the eigenvector corresponding to its largest eigenvalue, with the underlying idea that one’s own importance depends on the importance of the nodes to which one is connected.
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Acknowledgements
The present study is an extended version of a paper presented at the 19th International Conference on Scientometrics and Informetrics 2023 (ISSI 2023), Bloomington, Indiana (USA), 2-5 July 2023 (Tian et al., 2023). This study is partially supported by the National Natural Science Foundation of China (71974029, 71974030) and LiaoNing Revitalization Talents Program (XLYC2007149). Wencan Tian is financially supported by the China Scholarship Council (202106060134). The authors are grateful to the anonymous reviewers for their helpful comments and suggestions.
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See Fig. 7.
Results of robustness test. Both reducing and raising the maximum lag time demonstrate a strong statistically significant causal association between article usage and publication counts, demonstrating that the results from this study are robust. Specifically, when the maximum lag was set to 10, 76.8% of the topics exhibited an inherent logical link between article usage and publication counts; when the maximum lag was set to 14, this proportion was 84.3%
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Tian, W., Wang, Y., Hu, Z. et al. Does Granger causality exist between article usage and publication counts? A topic-level time-series evidence from IEEE Xplore. Scientometrics 129, 3285–3302 (2024). https://doi.org/10.1007/s11192-024-05038-8
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DOI: https://doi.org/10.1007/s11192-024-05038-8