How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications

TL;DR

This paper introduces a new framework to evaluate how citations are used to engage with multiple disciplines in interdisciplinary NLP research, addressing limitations of existing methods.

Contribution

It proposes a tailored citation purpose taxonomy and an annotation study, enabling quantitative assessment of interdisciplinary citation engagement.

Findings

The framework effectively characterizes citation engagement in NLP and Social Science interdisciplinary work.

It provides a quantitative measure of citation purpose aligned with interdisciplinary research.

The analysis reveals patterns of citation use that reflect meaningful interdisciplinary engagement.

Abstract

With the rising popularity of interdisciplinary work and increasing institutional incentives in this direction, there is a growing need to understand how resulting publications incorporate ideas from multiple disciplines. Existing computational approaches, such as affiliation diversity, keywords, and citation patterns, do not account for how individual citations are used to advance the citing work. Although, in line with addressing this gap, prior studies have proposed taxonomies to classify citation purpose, these frameworks are not well-suited to interdisciplinary research and do not provide quantitative measures of citation engagement quality. To address these limitations, we propose a framework for the evaluation of citation engagement in interdisciplinary Natural Language Processing (NLP) publications. Our approach introduces a citation purpose taxonomy tailored to interdisciplinary work, supported by an annotation study. We demonstrate the utility of this framework through a thorough analysis of publications at the intersection of NLP and Computational Social Science.