Developing a topic-driven method for interdisciplinarity analysis

Abstract

• This study presented a methodological framework for analyzing topic-based interdisciplinarity. The proposed bottom-up approach effectively describes the relationships and structure of disciplines centered on particular research topics. • A text corpus of keywords with important semantic meanings that did not appear in publications was generated through a deep keyword generation model from the vast number of articles. Dirichlet-Multinomial Regression topic modeling, interdisciplinarity indices, and network analysis were employed to analyze the collected corpus. • Our methodology represents the current dynamic and convergent knowledge system in a bottom-up manner. Also, through the four interdisciplinarity indices, we not only measured the similarity of keywords shared by disciplines within each topic but also analyzed the relationships between topics based on keyword co-occurrence. • Our proposed framework is not limited to disciplines but serves as a guide to uncover the characteristics of topics and relationships between topics that are actively discussed in a research domain with high interdisciplinarity (e.g., literacy). Therefore, this study is significant by presenting a methodology that reveals topics with high collaboration potential and their relationships using keywords in over 200 disciplines. 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.