Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines
Hiroki Sayama, Jin Akaishi

TL;DR
This study uses web search engines to analyze researcher networks and research topics, revealing that interdisciplinarity is a multi-dimensional concept correlated with visibility boosts and network centrality.
Contribution
It introduces a novel web search engine-based method to characterize researcher-topic relatedness and interdisciplinarity, expanding beyond traditional citation and co-authorship analyses.
Findings
Visibility boosts are positively correlated with individual-level interdisciplinarity.
Visibility boosts related to network topics are positively correlated with social-level interdisciplinarity.
Interdisciplinarity is a multi-dimensional concept, requiring multiple assessment methods.
Abstract
Researchers' networks have been subject to active modeling and analysis. Earlier literature mostly focused on citation or co-authorship networks reconstructed from annotated scientific publication databases, which have several limitations. Recently, general-purpose web search engines have also been utilized to collect information about social networks. Here we reconstructed, using web search engines, a network representing the relatedness of researchers to their peers as well as to various research topics. Relatedness between researchers and research topics was characterized by visibility boost-increase of a researcher's visibility by focusing on a particular topic. It was observed that researchers who had high visibility boosts by the same research topic tended to be close to each other in their network. We calculated correlations between visibility boosts by research topics and…
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