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A Simple, interpretable method to identify surprising topic shifts in scientific fields.

Lu Cheng1, Jacob G Foster2, Harlin Lee1

  • 1Department of Mathematics, University of California, Los Angeles, Los Angeles, CA, United States.

Frontiers in Research Metrics and Analytics
|October 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a text-mining framework to track evolving scientific topics, identifying vanishing and emerging themes in cognitive science. The method reveals shifts, like the decline of the AI debate and rise of art and technology topics.

Keywords:
cognitive scienceentropymatrix factorizationscience of sciencetopic matchingtopic modeling

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cognitive science is highly interdisciplinary, making it challenging to track evolving research trends.
  • The revival of neural networks in AI/ML around 2012 may have influenced cognitive science.
  • Identifying shifts in scientific discourse is crucial for understanding field evolution.

Purpose of the Study:

  • To develop and validate a text-mining framework for systematically identifying vanishing and newly formed topics in scientific fields.
  • To analyze changes in cognitive science research topics before and after 2012, coinciding with AI/ML advancements.
  • To quantify topical shifts using an entropy-based measure.

Main Methods:

  • Application of topic modeling using non-negative matrix factorization (NMF) on cognitive science publications.
  • Representation of distinct topic sets in a common, interpretable vector space.
  • Utilizing an entropy-based measure to quantify the degree of topical shifts over time.

Main Results:

  • The framework successfully identified vanishing topics, such as the connectionist/symbolic AI debate.
  • Newly emerged topics, including the intersection of art and technology, were also detected.
  • Quantifiable evidence of topical shifts in cognitive science post-2012 was established.

Conclusions:

  • The proposed text-mining framework is effective for tracking topic evolution in diverse scientific fields.
  • The method provides insights into how external scientific developments (e.g., AI/ML) can influence adjacent disciplines.
  • This approach can aid in making more efficient and impactful scientific discoveries by highlighting research trends.