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| 时间切片主题演化分析× | 共词分析× | |
|---|---|---|
| 领域 | 科学计量学 | 科学计量学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2011–2012 | 1983 |
| 提出者≠ | Cobo, López-Herrera, Herrera-Viedma & Herrera | Michel Callon, Jean-Pierre Courtial, and colleagues |
| 类型≠ | Longitudinal bibliometric analysis | Scientometric network analysis technique |
| 开创性文献≠ | Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. DOI ↗ | Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235. DOI ↗ |
| 别名 | longitudinal thematic mapping, temporal thematic evolution, time-period thematic analysis, diachronic science mapping | keyword co-occurrence analysis, co-word mapping, keyword co-word network, CWA |
| 相关 | 6 | 6 |
| 摘要≠ | Time-sliced thematic evolution analysis is a bibliometric method that divides a corpus of publications into consecutive time windows and tracks how research themes emerge, consolidate, split, merge, or disappear across those periods. By applying co-word analysis and strategic-diagram mapping within each slice and then linking themes across slices, it reveals the intellectual trajectory of a research field over time. | Co-word analysis is a scientometric technique that quantifies how often pairs of keywords, subject terms, or title words appear together across a corpus of publications. By treating simultaneous occurrence as a proxy for conceptual relatedness, it constructs networks and clusters that reveal the intellectual structure, dominant themes, and emerging sub-fields of a research domain. |
| ScholarGate数据集 ↗ |
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