@inproceedings{chamoun-etal-2025-social,
title = "Social Good or Scientific Curiosity? Uncovering the Research Framing Behind {NLP} Artefacts",
author = "Chamoun, Eric and
Ousidhoum, Nedjma and
Schlichtkrull, Michael Sejr and
Vlachos, Andreas",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1286/",
pages = "25310--25346",
ISBN = "979-8-89176-332-6",
abstract = "Clarifying the research framing of NLP artefacts (e.g., models, datasets, etc.) is crucial to aligning research with practical applications when researchers claim that their findings have real-world impact. Recent studies manually analyzed NLP research across domains, showing that few papers explicitly identify key stakeholders, intended uses, or appropriate contexts. In this work, we propose to automate this analysis, developing a three-component system that infers research framings by first extracting key elements (means, ends, stakeholders), then linking them through interpretable rules and contextual reasoning.We evaluate our approach on two domains: automated fact-checking using an existing dataset, and hate speech detection for which we annotate a new dataset{---}achieving consistent improvements over strong LLM baselines.Finally, we apply our system to recent automated fact-checking papers and uncover three notable trends: a rise in underspecified research goals, increased emphasis on scientific exploration over application, and a shift toward supporting human fact-checkers rather than pursuing full automation."
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<abstract>Clarifying the research framing of NLP artefacts (e.g., models, datasets, etc.) is crucial to aligning research with practical applications when researchers claim that their findings have real-world impact. Recent studies manually analyzed NLP research across domains, showing that few papers explicitly identify key stakeholders, intended uses, or appropriate contexts. In this work, we propose to automate this analysis, developing a three-component system that infers research framings by first extracting key elements (means, ends, stakeholders), then linking them through interpretable rules and contextual reasoning.We evaluate our approach on two domains: automated fact-checking using an existing dataset, and hate speech detection for which we annotate a new dataset—achieving consistent improvements over strong LLM baselines.Finally, we apply our system to recent automated fact-checking papers and uncover three notable trends: a rise in underspecified research goals, increased emphasis on scientific exploration over application, and a shift toward supporting human fact-checkers rather than pursuing full automation.</abstract>
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%0 Conference Proceedings
%T Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts
%A Chamoun, Eric
%A Ousidhoum, Nedjma
%A Schlichtkrull, Michael Sejr
%A Vlachos, Andreas
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F chamoun-etal-2025-social
%X Clarifying the research framing of NLP artefacts (e.g., models, datasets, etc.) is crucial to aligning research with practical applications when researchers claim that their findings have real-world impact. Recent studies manually analyzed NLP research across domains, showing that few papers explicitly identify key stakeholders, intended uses, or appropriate contexts. In this work, we propose to automate this analysis, developing a three-component system that infers research framings by first extracting key elements (means, ends, stakeholders), then linking them through interpretable rules and contextual reasoning.We evaluate our approach on two domains: automated fact-checking using an existing dataset, and hate speech detection for which we annotate a new dataset—achieving consistent improvements over strong LLM baselines.Finally, we apply our system to recent automated fact-checking papers and uncover three notable trends: a rise in underspecified research goals, increased emphasis on scientific exploration over application, and a shift toward supporting human fact-checkers rather than pursuing full automation.
%U https://aclanthology.org/2025.emnlp-main.1286/
%P 25310-25346
Markdown (Informal)
[Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts](https://aclanthology.org/2025.emnlp-main.1286/) (Chamoun et al., EMNLP 2025)
ACL