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<p>Population ageing poses significant challenges globally, particularly regarding the rapid growth of the older adult population. To map the intellectual structure of this field, this study conducts a bibliometric analysis of 691 highly cited papers, defined as those ranking in the top 1% by citations according to Essential Science Indicators (ESI), published between 2014 and 2024 in the Web of Science Core Collection. Utilizing CiteSpace, we employed co-authorship analysis to examine collaboration networks and keyword co-occurrence with burst detection to identify research hotspots and emerging trends. The Log-Likelihood Ratio (LLR) algorithm was applied to label distinct clusters. Quantitative validation of the network structure yielded a Modularity Q of 0.4585 and a Mean Silhouette of 0.7592, indicating a highly significant and reliable clustering structure. The results reveal that research on older adults is multidisciplinary, with major contributions from the United States and China. However, collaboration among scholars and institutions remains relatively fragmented. This study provides a rigorous, data-driven overview of the domain, offering valuable insights for strengthening academic collaboration and guiding future policy for older adults.</p>

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Knowledge Mapping and Visualization of Ageing Research Based on CiteSpace: An Analysis of Highly Cited Publications in the Web of Science Database (2014–2024)

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Bowen Shi,
  • Xiaoxia Xu,
  • Mingrui Fan

摘要

Population ageing poses significant challenges globally, particularly regarding the rapid growth of the older adult population. To map the intellectual structure of this field, this study conducts a bibliometric analysis of 691 highly cited papers, defined as those ranking in the top 1% by citations according to Essential Science Indicators (ESI), published between 2014 and 2024 in the Web of Science Core Collection. Utilizing CiteSpace, we employed co-authorship analysis to examine collaboration networks and keyword co-occurrence with burst detection to identify research hotspots and emerging trends. The Log-Likelihood Ratio (LLR) algorithm was applied to label distinct clusters. Quantitative validation of the network structure yielded a Modularity Q of 0.4585 and a Mean Silhouette of 0.7592, indicating a highly significant and reliable clustering structure. The results reveal that research on older adults is multidisciplinary, with major contributions from the United States and China. However, collaboration among scholars and institutions remains relatively fragmented. This study provides a rigorous, data-driven overview of the domain, offering valuable insights for strengthening academic collaboration and guiding future policy for older adults.