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

Dias, L., Gerlach, M., Scharloth, J., & Altmann, E. G. (2018). Using text analysis to quantify the similarity and evolution of scientific disciplines. Royal Society Open Science, 5(1): 171545. doi:10.1098/rsos.171545.

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 Creators:
Dias, Laércio1, Author           
Gerlach, Martin1, Author           
Scharloth, Joachim2, Author
Altmann, Eduardo G.1, Author           
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1Max Planck Institute for the Physics of Complex Systems, Max Planck Society, ou_2117288              
2External Organizations, ou_persistent22              

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 MPIPKS: Living matter
 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.

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 Dates: 2018-01-172018
 Publication Status: Issued
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 Identifiers: DOI: 10.1098/rsos.171545
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Title: Royal Society Open Science
Source Genre: Journal
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Publ. Info: London : Royal Society
Pages: - Volume / Issue: 5 (1) Sequence Number: 171545 Start / End Page: - Identifier: Other: 2054-5703
CoNE: https://pure.mpg.de/cone/journals/resource/2054-5703