@inproceedings{sudharsan-etal-2026-engage,
title = "How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications",
author = "Sudharsan, Bagyasree and
Leto, Alexandria and
Pacheco, Maria Leonor",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2290/",
pages = "49070--49090",
ISBN = "979-8-89176-462-0",
abstract = "With the rising popularity of interdisciplinary work, there is a growing need to understand how publications incorporate ideas from multiple disciplines. Existing computational approaches, such as affiliation diversity, keyword matching, and citation patterns, do not account for how individual citations are used to advance the citing work. Prior studies have proposed taxonomies to classify citation purpose. However, these frameworks are not well-suited to interdisciplinary research and do not provide general measures of engagement depth. To address these limitations, we propose a framework for evaluating citation engagement in scholarly publications. We introduce a citation purpose taxonomy tailored to interdisciplinary work, supported by an annotation study and model for automatically predicting a citation{'}s purpose. We then leverage signal from a citation{'}s purpose and the sections in which it appears to predict a single measure of how deeply a publication engages with a citation. We demonstrate the utility of our framework through a large-scale analysis of publications at the intersection of NLP and Computational Social Science."
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<abstract>With the rising popularity of interdisciplinary work, there is a growing need to understand how publications incorporate ideas from multiple disciplines. Existing computational approaches, such as affiliation diversity, keyword matching, and citation patterns, do not account for how individual citations are used to advance the citing work. Prior studies have proposed taxonomies to classify citation purpose. However, these frameworks are not well-suited to interdisciplinary research and do not provide general measures of engagement depth. To address these limitations, we propose a framework for evaluating citation engagement in scholarly publications. We introduce a citation purpose taxonomy tailored to interdisciplinary work, supported by an annotation study and model for automatically predicting a citation’s purpose. We then leverage signal from a citation’s purpose and the sections in which it appears to predict a single measure of how deeply a publication engages with a citation. We demonstrate the utility of our framework through a large-scale analysis of publications at the intersection of NLP and Computational Social Science.</abstract>
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%0 Conference Proceedings
%T How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications
%A Sudharsan, Bagyasree
%A Leto, Alexandria
%A Pacheco, Maria Leonor
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sudharsan-etal-2026-engage
%X With the rising popularity of interdisciplinary work, there is a growing need to understand how publications incorporate ideas from multiple disciplines. Existing computational approaches, such as affiliation diversity, keyword matching, and citation patterns, do not account for how individual citations are used to advance the citing work. Prior studies have proposed taxonomies to classify citation purpose. However, these frameworks are not well-suited to interdisciplinary research and do not provide general measures of engagement depth. To address these limitations, we propose a framework for evaluating citation engagement in scholarly publications. We introduce a citation purpose taxonomy tailored to interdisciplinary work, supported by an annotation study and model for automatically predicting a citation’s purpose. We then leverage signal from a citation’s purpose and the sections in which it appears to predict a single measure of how deeply a publication engages with a citation. We demonstrate the utility of our framework through a large-scale analysis of publications at the intersection of NLP and Computational Social Science.
%U https://aclanthology.org/2026.findings-emnlp.2290/
%P 49070-49090
Markdown (Informal)
[How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications](https://aclanthology.org/2026.findings-emnlp.2290/) (Sudharsan et al., Findings 2026)
ACL