Using text analysis to quantify the similarity and evolution of scientific disciplines

Laércio Dias, Martin Gerlach, Joachim Scharloth, Eduardo G. Altmann

Research output: Contribution to journalArticle

6 Citations (Scopus)

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 of disciplines 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.

Original languageEnglish
Article number171545
JournalRoyal Society Open Science
Volume5
Issue number1
DOIs
Publication statusPublished - 2018 Jan 17
Externally publishedYes

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temporal analysis
divergence
science
analysis

Keywords

  • Dissimilarity measures
  • Information theory
  • Science of science

ASJC Scopus subject areas

  • General

Cite this

Using text analysis to quantify the similarity and evolution of scientific disciplines. / Dias, Laércio; Gerlach, Martin; Scharloth, Joachim; Altmann, Eduardo G.

In: Royal Society Open Science, Vol. 5, No. 1, 171545, 17.01.2018.

Research output: Contribution to journalArticle

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