Developing a topic-driven method for interdisciplinarity analysis

Hyeyoung Kim, Hyelin Park, Min Song

Research output: Contribution to journalArticlepeer-review


This study explores the topic-based interdisciplinarity in the research domain of literacy. A text corpus of keywords was generated through a deep keyword generation model from abstracts of 346,387 articles published in 296 disciplines from 1917 to 2021. Dirichlet-Multinomial Regression topic modeling, interdisciplinarity indices, and network analysis were employed to analyze the collected corpus. Topic modeling uncovered 15 dominant research topics in the literacy field, as well as their up-and-down trends from 2000 to 2021. For each topic, keywords were then replaced with disciplines, and interdisciplinarity was measured using four indices: variety, balance, disparity, and diversity. Finally, the interdisciplinarity of each topic, connectivity between topics, and topic trends were comprehensively analyzed on the keyword co-occurrence network. Our methodology reaches beyond connectivity limited to a few disciplines and provides insight into the direction of collaboration between disciplines centered on a research domain. Moreover, the study's deep keyword generation model has methodological implications for forming a corpus spanning numerous disciplines as a bottom-up approach.

Original languageEnglish
Article number101255
JournalJournal of Informetrics
Issue number2
Publication statusPublished - 2022 May

Bibliographical note

Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea ( NRF-2020S1A5B1104865 ).

Publisher Copyright:
© 2022

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Library and Information Sciences


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