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| 研究前沿识别× | 关键词共现分析× | |
|---|---|---|
| 领域 | 文献计量学 | 文献计量学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s | 2000s |
| 提出者≠ | Chaomei Chen and others | Bibliometric research community |
| 类型 | Method | Method |
| 开创性文献≠ | Chen, C., & Paul, R. J. (1997). Visualizing a knowledge domain's intellectual structure. IEEE Computer, 30(3), 65–71. link ↗ | Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166. DOI ↗ |
| 别名 | emerging research detection, research frontier mapping, hot topic identification, emerging field analysis | term co-occurrence, keyword network analysis, thematic analysis, term clustering |
| 相关≠ | 5 | 4 |
| 摘要≠ | Research front identification is a bibliometric method for detecting emerging or cutting-edge research areas within a larger research landscape. A 'research front' is a cluster of recently published, highly-cited papers that define the current active research direction in a field. Unlike established research communities (identifiable through co-citation networks and slow-changing patterns), research fronts are characterized by rapid growth, high citation velocity (papers accumulating citations quickly), and weak historical ties to established literature. Developed systematically by Chen and others in the 1990s–2000s, research front identification enables researchers, funders, and policy makers to track where scientific activity is concentrating and where breakthrough research is emerging. | Keyword co-occurrence analysis is a text mining and bibliometric method that identifies research themes and their relationships by analyzing how frequently terms or keywords appear together in abstracts, titles, or indexed keywords of scientific publications. When two keywords appear together frequently, they are considered co-occurring, indicating a shared thematic or conceptual relationship. This method rapidly reveals the topical structure of a research field without relying on formal classifications, making it particularly useful for detecting emerging research areas and understanding disciplinary boundaries. |
| ScholarGate数据集 ↗ |
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