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

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Dias,  Laércio
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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Gerlach,  Martin
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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Altmann,  Eduardo G.
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0000-C6ED-6
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.