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.

翻译：随着跨学科工作的日益普及以及相关机构激励措施的不断增加，理解由此产生的出版物如何整合多学科思想的需求日益增长。现有的计算方法，如机构多样性分析、关键词统计和引文模式分析，并未考虑个体引文如何被用于推进引用工作的学术论证。尽管已有研究针对这一空白提出了引文目的分类法，但这些框架并不适用于跨学科研究，且未能提供引文参与质量的量化指标。为弥补这些不足，我们提出了一个用于评估跨学科自然语言处理（NLP）出版物中引文参与度的框架。该方法通过标注研究支持，构建了适用于跨学科工作的引文目的分类体系。我们通过对NLP与计算社会科学交叉领域出版物的深入分析，验证了该框架的实用性。