Quantifying hot topic dynamics in scientific literature: An information-theoretical approach

Area of Science:

• Computational Social Science
• Bibliometrics
• Information Science

Background:

• Understanding scientific discourse evolution is key for tracking research trends.
• Existing methods like topic modeling struggle with fine-grained semantic shifts and computational demands.
• Co-occurrence networks lack metric properties for rigorous temporal analysis.

Purpose of the Study:

• To develop a metric-based framework for analyzing evolving concept networks in scientific literature.
• To address limitations of existing methods in capturing semantic shifts and enabling temporal comparisons.
• To provide a scalable and interpretable approach for tracking concept dynamics.

Main Methods:

• Utilized 10,370 articles (2010-2023) on international security from JSTOR and PORTICO.
• Computed normalized variation of information (NVI) distances to build annual concept networks.
• Quantified semantic change using velocity matrices and Minimum Spanning Tree (MST) analysis.

Main Results:

• Conceptual shifts are concentrated in localized temporal hubs.
• Semantic change is driven by contextual information and shared uncertainty, not solely co-occurrence frequency.
• Identified major trends in the reconfiguration of knowledge over time.

Conclusions:

• The proposed framework offers a scalable, interpretable, and mathematically grounded method for analyzing concept dynamics.
• This approach provides new tools for topic evolution analysis in scientific literature.
• Contributes insights into the structural organization and reconfiguration of knowledge over time.