Using text analysis to quantify the similarity and evolution of scientific disciplines
arXiv:1706.08671 · doi:10.1098/rsos.171545
Abstract
We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20M papers from the past three decades reveals that the linguistic similarity is related but different from experts and citation-based classifications, leading to an improved view on the organization of science. A temporal analysis of the similarity of fields shows that some fields (e.g., computer science) are becoming increasingly central, but that on average the similarity between pairs has not changed in the last decades. This suggests that tendencies of convergence (e.g., multi-disciplinarity) and divergence (e.g., specialization) of disciplines are in balance.
arXiv comment9 pages, 4 figures
References in corpus (13)
- Maps of random walks on complex networks reveal community structure
- Quantifying Long-Term Scientific Impact
- The ground truth about metadata and community detection in networks
- Science Concierge: A fast content-based recommendation system for scientific publications
- Using network science and text analytics to produce surveys in a scientific topic
- The evolution of interdisciplinarity in physics research
- Using text analysis to quantify the similarity and evolution of scientific disciplines
- The Distribution of the Asymptotic Number of Citations to Sets of Publications by a Researcher or From an Academic Department Are Consistent With a Discrete Lognormal Model
- Similarity of symbol frequency distributions with heavy tails
- Evaluating the impact of interdisciplinary research: a multilayer network approach
- Impact of lexical and sentiment factors on the popularity of scientific papers
- Generalized Entropies and the Similarity of Texts
- Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines
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