ArticleFrontiers in research metrics and analytics2022
A Simple, interpretable method to identify surprising topic shifts in scientific fields.
Article in Frontiers in research metrics and analytics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- SCINOBO: a novel system classifying scholarly communication in a dynamically constructed hierarchical Field-of-Science taxonomy.Frontiers in research metrics and analytics · 2023Article
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3 authors.
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Abstract
This paper proposes a text-mining framework to systematically identify vanishing or newly formed topics in highly interdisciplinary and diverse fields like cognitive science. We apply topic modeling via non-negative matrix factorization to cognitive science publications before and after 2012; this allows us to study how the field has changed since the revival of neural networks in the neighboring field of AI/ML. Our proposed method represents the two distinct sets of topics in an interpretable, common vector space, and uses an entropy-based measure to quantify topical shifts. Case studies on vanishing (e.g., connectionist/symbolic AI debate) and newly emerged (e.g., art and technology) topics are presented. Our framework can be applied to any field or any historical event considered to mark a major shift in thought. Such findings can help lead to more efficient and impactful scientific discoveries.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.