量化科学文献中的热门话题动态:一种信息理论方法
1Centre for European Regional and Local Studies (EUROREG), Science Studies Laboratory, University of Warsaw, Warsaw, Poland.
PloS one
|July 8, 2025
概括
这项研究引入了一个新的基于指标的框架来分析科学文献中不断演变的概念网络,揭示了概念转变是由上下文驱动的,而不仅仅是频率.
科学领域:
- 计算社会科学 计算社会科学
- 图书统计学 图书统计学
- 信息科学 信息科学 信息科学
背景情况:
- 了解科学话语演变是跟踪研究趋势的关键.
- 像主题建模这样的现有方法与细粒度的语义转移和计算需求作斗争.
- 同时发生的网络缺乏用于严格的时间分析的度量属性.
研究的目的:
- 开发一个基于指标的框架来分析科学文献中不断演变的概念网络.
- 解决现有方法在捕捉语义转移和实现时间比较方面的局限性.
- 为跟踪概念动态提供可扩展和可解释的方法.
主要方法:
- 从JSTOR和PORTICO中使用了10370篇 (2010-2023) 关于国际安全的文章.
- 计算信息规范变化 (NVI) 距离,以建立年度概念网络.
- 使用速度矩阵和最小跨度树 (MST) 分析量化语义变化.
主要成果:
- 概念变化集中在局部的时间枢纽.
- 语义变化是由上下文信息和共享的不确定性驱动的,而不仅仅是同时发生的频率.
- 识别了随着时间的推移对知识的重新配置的主要趋势.
结论:
- 拟议的框架为分析概念动态提供了一个可扩展,可解释和数学基础的方法.
- 这种方法为科学文献中主题演变分析提供了新的工具.
- 贡献了对知识的结构组织和随着时间的推移而重新配置的见解.
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