A First Step towards Measuring Interdisciplinary Engagement in Scientific Publications: A Case Study on NLP + CSS Research

Alexandria Leto, Shamik Roy, Alexander Hoyle, Daniel Acuna, Maria Leonor Pacheco

Abstract
With the rise in the prevalence of cross-disciplinary research, there is a need to develop methods to characterize its practices. Current computational methods to evaluate interdisciplinary engagement—such as affiliation diversity, keywords, and citation patterns—are insufficient to model the degree of engagement between disciplines, as well as the way in which the complementary expertise of co-authors is harnessed. In this paper, we propose an automated framework to address some of these issues on a large scale. Our framework tracks interdisciplinary citations in scientific articles and models: 1) the section and position in which they appear, and 2) the argumentative role that they play in the writing. To showcase our framework, we perform a preliminary analysis of interdisciplinary engagement in published work at the intersection of natural language processing and computational social science in the last decade.
Anthology ID:
2024.nlpcss-1.11
Volume:
Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Dallas Card, Anjalie Field, Dirk Hovy, Katherine Keith
Venues:
NLP+CSS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
144–158
Language:
URL:
https://aclanthology.org/2024.nlpcss-1.11
DOI:
Bibkey:
Cite (ACL):
Alexandria Leto, Shamik Roy, Alexander Hoyle, Daniel Acuna, and Maria Leonor Pacheco. 2024. A First Step towards Measuring Interdisciplinary Engagement in Scientific Publications: A Case Study on NLP + CSS Research. In Proceedings of the Sixth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS 2024), pages 144–158, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
A First Step towards Measuring Interdisciplinary Engagement in Scientific Publications: A Case Study on NLP + CSS Research (Leto et al., NLP+CSS-WS 2024)
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